Multistage reception of code division multiple access transmissions

ABSTRACT

Reducing multiple access interference in the radio frequency communications path between the network base station and UE. The preferred methods include interleaving STAP and MUD in a plurality of stages in a base station receiver. Specific embodiments accomplish this by selecting certain UE connections for MUD processing by stage. Then, in two or more stages, demodulating the UE connections selected for MUD processing using STAP, and MUD canceling UE connections selected for MUD processing. Within the latency requirements of the network, preferred embodiments of the present invention also command a transmit power for each UE connections in order to exploit the effect of interleaved STAP and MUD processing.

CROSS-REFERENCE TO RELATED APPLICATIONS

This non-provisional patent application is a continuation of U.S. patentapplication Ser. No. 10/108,569, filed Mar. 29, 2002 now U.S. Pat. No.7,209,515, entitled “Multistage Reception Of Code Division MultipleAccess Transmissions”, which claims benefit of U.S. Provisional PatentApplication No. 60/279,673 filed Mar. 30, 2001, both of which areincorporated herein by reference in their entirety.

FIELD OF THE INVENTION

This invention relates generally to spread spectrum communicationsystems, and more particularly to the use of specialized interferencecancellation techniques, including an novel combination of Space TimeAdaptive Processing (STAP) and Multi-User Detection (MUD), to reduce MAIand improve the capacity of a spread spectrum-based cellular system.

BACKGROUND OF THE INVENTION

Multiple Access Interference (MAI) is a significant source ofinterference (noise) that limits the capacity and performance of CodeDivision Multiple Access (CDMA) wireless services. Higher data rateservices like those provided by Wideband CDMA (WCDMA) systems exacerbatethis situation as processing gain is reduced and must be compensated forby higher power (signal) levels, which in turn limits capacity for thoseapplications.

A number of techniques have been conceived for mitigating MAI in spreadspectrum wireless systems and thus improving overall networkperformance. The proposed solutions may be grouped into at least threeclasses: adaptive antenna array technology, multi-user receivers, andrapid power control. One class of these techniques employs adaptivearray techniques along with specialized adaptive processing to improvenetwork performance. U.S. Pat. Nos. 6,154,485; 6,141,567; 6,115,409;6,108,565; 6,100,843; 6,061,553; 6,031,877; 5,930,243; 5,904,470; and5,828,658 are examples of these techniques.

Another class of techniques utilizes complex algorithms in the receiverto concurrently estimate the signals from multiple users utilizing atechnique referred to as MUD or variants thereof. U.S. Pat. Nos.6,137,843; 6,108,564; 6,081,516; 6,014,373; 6,002,727; 5,956,333; and5,719,852 are examples of these technological schemes.

A third class of techniques involves network link performance monitoringand control functions including monitoring and controlling transmittedpowers from the base station and the mobile station and/or monitoringand controlling link signal quality metrics to mitigate MAI and improveoverall network performance. Examples of this technology include U.S.Pat. Nos. 6,167,031; 6,131,049; 6,157,619; 6,119,010; 6,118,983;6,104,933; and 6,088,335.

While the above-referenced patents disclose ways to improve WCDMAnetwork performance within the specific technology (adaptive antennasand adaptive processing, MUD filtering techniques, or network qualityperformance monitoring and enhancement), they do not address thosetechnological opportunities that collectively improve system performanceacross the entire receiver architecture.

Some aspects of this invention which separate it from other publishedand patented systems and methods are an integrated receiver architectureconfigured with a suite of signal processing algorithms incorporatingthe power of both STAP and MUD and the real-time control algorithms thatassigns/allocates processing requirements. This architecture seeks,among other things, to optimize WCDMA network performance againstpractical real-time system constraints, notably computational complexity(cost) and network performance (throughput), and latency.

SUMMARY OF THE INVENTION

Preferred embodiments of the present invention are placed in the contextof a communications network and include methods of reducing multipleaccess interference in the radio frequency communications path betweenthe network base station and UE. The preferred methods include STAP andMUD in a plurality of stages in a base station receiver. Specificembodiments accomplish this by selecting certain UE connections for MUDprocessing by stage. Then, in two or more stages, demodulating the UEconnections selected for MUD processing using STAP, and MUD canceling UEconnections selected for MUD processing.

Within the latency requirements of the network, preferred embodiments ofthe present invention also command a transmit power for each UEconnections in order to exploit the effect of interleaved STAP and MUDprocessing.

In further embodiments, the invention includes a method of reducingmultiple access interference where in a first frame, UE requesting aconnection having bandwidth greater than a voice-grade channel areprovided an aggregate physical channel bandwidth less than thatrequested.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating relationships between functionalaspects of a preferred embodiment of the present invention.

FIG. 2 is a block diagram illustrating relationships between functionalaspects of a DEMOD UE function block of a preferred embodiment of thepresent invention.

FIG. 3 illustrates results of a simulation of the present inventioncomparing capacity increase versus MAI reduction for various data ratesusing a preferred embodiment of the present invention.

FIG. 4 illustrates the conceptual flow of a simulation the performanceof preferred embodiments of the present invention.

FIG. 5 illustrates results of a simulation comparing network loading forvoice grade channels.

FIG. 6 illustrates results of a simulation comparing network loading fora varied QoS mix.

FIG. 7 illustrates results of a simulation of capacity versus coveragearea for a cell populated by voice-grade users using conventional aconventional WCDMA receiver and a receiver implementing a preferredembodiment of the present invention.

FIG. 8 illustrates results of a simulation of capacity versus coveragearea for a cell populated by data users (various rates) usingconventional a conventional WCDMA receiver and a receiver implementing apreferred embodiment of the present invention.

FIG. 9 illustrates results of a simulation of capacity versus coveragearea for a cell populated by a mix of voice and data users (variousrates) using conventional a conventional WCDMA receiver and a receiverimplementing a preferred embodiment of the present invention.

FIG. 10 illustrates results of a simulated comparison, for a mix ofvoice and date users, of the capacity and noise levels associated with:a macro-cell solution employing a conventional WCDMA receiver, amacro-cell solution employing a receiver implementing a preferredembodiment of the present invention, and a micro-cell solution employingconventional WCDMA receivers.

FIG. 11 illustrates processing complexity associated with preferredembodiments of the present invention.

FIG. 12 illustrates a relationship between number of MUD processes andSTAP gain for 37 cell networks of 384 kbps data users for a preferredembodiment of the present invention.

FIG. 13 illustrates a MUD processing algorithm for one UE connection inaccordance with a preferred embodiment of the present invention.

FIG. 14 illustrates the results of a simulation of a preferredembodiment of the present invention.

FIG. 15 illustrates a comparison of SINR performance for WCDMA voiceservice for a one antenna element conventional system and a four-elementsystem employing STAP.

FIG. 16 illustrates a comparison of SINR performance for WCDMA voice anddata service for various configurations of STAP and number of antennaelements.

FIG. 17 illustrates a benefit of two-stage parallel MUD processing.

FIG. 18 is a block diagram illustrating relationships between functionalaspects of a preferred embodiment of a link manager of the presentinvention.

FIG. 19 is a functional block diagram illustrating generation of metricsfor an inner power control loop in accordance with a preferredembodiment of the present invention.

FIG. 20 is a functional block diagram illustrating compensation for lackof STAP gain in a power control loop of preferred embodiments of thepresent invention.

FIG. 21 is a functional block diagram illustrating an outer powercontrol loop of preferred embodiments of the present invention.

FIG. 22 illustrates a channel self-coherence function and delay spreadspectrum function of preferred embodiments of the present invention.

FIG. 23 is a block diagram of a design architecture of a reverse linkprocessing system of the present invention.

FIG. 24 is a block diagram of a design architecture of a reverse linkprocessor of the present invention.

FIG. 25 a block diagram of a design architecture of a slot processor ofthe present invention.

FIG. 26 is a block diagram of a design architecture of a stage processorof the present invention.

FIG. 27 is a block diagram of a design architecture of a user processorof the present invention.

DETAILED DESCRIPTION OF THE INVENTION

Referring to FIG. 1, a WCDMA receiver architecture 100 is disclosed thatincludes a space-time adaptive processing (STAP) and multi-userdetection (MUD) filter architecture, a MUD Controller 400 that allocatesMUD processes to in-cell users and users in soft handoff, and a LinkManager 500 that levies real time requirements on radio resource andquality of service (QoS) management algorithms in a base station. Thearchitecture enables elements of the receiver (filters, MUD Controller400, and Link Manager 500) to operate in a manner that offersimprovements in performance measures such as interference mitigation,signal equalization, and signal fading compensation in accordance withnear-real-time assessments of network loading and quality of servicemix, and physical channel conditions.

The WCDMA receiver architecture provides a nominal 5× base stationcapacity increase over the conventional WCDMA de-spreading anddemodulation processing as specified by the Third Generation PartnershipProject (3GPP). The architecture is fully compliant with the 3GPPspecification. Besides supporting increased operating revenues per basestation in the network, the receiver architecture also provides thecoverage to implement higher capacity WCDMA networks over amacro-cellular footprint without requiring additional micro and picocell sites.

An additional benefit of the integrated STAP/MUD architecture is thede-coupling of the network capacity for voice users from capacity forhigh speed data users. In a conventional WCDMA receiver, a 2 Mbps datauser contributes about 250 times more MAI than a single voice user.Without the use of MUD techniques, the high speed data users dominatethe MAI environment, and their impact directly limits the overall systemcapacity. This results in a system where the cost of the high speed dataconnection is 250 times that of a voice user. By selectively applyingMUD to the high speed data users, the overall system capacity isimproved, and the direct link between the number of data users and thenumber of voice users that can be supported is broken.

The integrated receiver architecture incorporates a combination ofspatial, temporal, and user-selective filtering, network activityawareness, and link layer control to reduce reverse link Multiple AccessInterference (MAI) in a wide-band CDMA network that supports voice andmulti-rate data connections, such as WCDMA and CDMA2000. FIG. 1illustrates the functional receiver design. The following description ofthe invention provides a summary of each function in FIG. 1. After thefunctional design description, the network capacity and coverageimprovements associated with the claimed invention are summarized.

Overview

Referring to FIG. 1, the Radio Frequency (RF) signals 10 arriving ateach reverse link antenna element 20 in a given base station sector areconditioned 30 prior to being processed by preferred embodiments of theinvention such that digitally over-sampled complex base-band data 40 isprovided to the receiver described herein. A common technique forconditioning the received RF signal 10 is to RF filter, down-convert tointermediate frequency (IF), digitize, band-pass filter, and digitallydown-convert to base-band. Digital base-band data 40 from each basetransceiver station (BTS) sector antenna comprises the payload input forthe preferred embodiments of the invention. Signaling and control dataare also made available to the receiver, via the Link Manager 500interface to Layer 3, and will be described later in this disclosure.The digital base-band signal over a specific time window can berepresented by the matrix X_(m,n) where m equals the number of antennaelements in the BTS sector of interest, and n equals the number ofdigital samples in the specified time window.

As illustrated in FIG. 1, modulated transmissions from user equipment(UE) are demodulated in time-sequenced stages in the digital receiver100; the Demodulate UE 201, 202, 203 function is further illustrated inFIG. 2. Demodulate UE 201, 202, 203 converts select received base-banddigital samples 40 to information symbols, Demodulate UE 201, 202, 203is applied to base-band data 40 (both before and after MUD filtering asindicated in the figures) for each UE in the BTS sector of interest,including users in neighboring cells that are in the state of softhand-off. The Demodulate UE 201, 202, 203 function includes STAPbeamforming 210, de-scrambling and de-spreading 220, de-interleaving230, and decoding 240. Bundling of transport channels occurs at a higherOpen Systems Interconnection (OSI) layer in the base station.

The Demodulate UE 201, 202, 203 function converts m antenna channels ofdigital base-band data 40 into several channels of information bits,including Dedicated Physical Control Channels (DPCCH) 222 and DedicatedPhysical Data Channels (DPDCH) 224. According to the Third GenerationPartnership (3GPP) WCDMA air interface specification, user applicationssuch as voice and specific data applications are configured as DPDCH's.See 3GPP Technical Specification 25.211 V3.40. Multiple DedicatedPhysical Channels can be associated with a single UE, and each channelis distinguished in the air interface by a different Orthogonal VariableRate Spreading Factor code (OVSF code). Assignment of the UE long code(scrambling code) and OVSF codes is performed at a higher Open SystemsInterconnection (OSI) layer in the BTS and conveyed to the Demodulate UE201, 202, 203 function (as an initialization vector) by the ConnectionManagement function (not described herein).

For those UE connections designated for MUD processing, the receivedforward error correction (FEC) symbols for each control and data channelare estimated immediately after de-spreading 220 in the Demodulate UE201, 202, 203 function. The estimated symbols 226 are sent to a MUDProcessor 301, 302 to support MUD parameter estimation. CRC data 242 andinformation symbols 244 for each channel estimated after decoding aresent to the Link Manager 500 to support calculation of MUD effectivenessmetrics.

STAP beamforming 210 is the first operation in the Demodulate UE 201,202, 203 function. The STAP beamformer 210 is a 2-dimensional filter forwhich the first dimension corresponds to antennas in an array on thecell tower, and the second dimension corresponds to time samples in thedigital baseband data 40 per antenna element. A STAP beamformer with mantennas and n time taps requires computation of a length m*n complexweight vector that is multiplied by the digital baseband data andaccumulated to produce one complex beamformed sample per digital datasample across antennas. A STAP beamformer weight vector is computed andapplied for each user connected to the network. Because application ofthe STAP beamformer weights is performed digitally, embodiments of theinvention include a type of digital beamforming, with no requirement foranalog signal combining.

The MUD Controller 400 monitors network MAI contributions by sectors andusers. Leveraging known quality-of-service (QoS) characteristics of eachUE and other specified observable quantities, the MUD Controller 400selects the UE transmissions to be processed by each MUD Processor 301,302. The selection process is designed to reduce network MAI, provideenhanced network capacity, and seek effective usage of the processingresources required to implement MUD.

The MUD Controller 400 receives a table of UE parameters from the LinkManager 500, and estimates the MAI contribution of the UE connections inthe cell and in soft handoff to the cell that the BTS controls. UEconnections that have adequate signal to interference plus noise ratio(SINR) to achieve the desired cancellation are identified and sorted byMAI contribution. The rank-ordered (by MAI contribution) list of UEconnections is used to select candidates for cancellation in each MUDProcessor 301, 302. The number of MUD processes executed depends on howmuch computational resources are dedicated to MUD processing in a givenBTS Sector. The MUD Controller 400 also determines which connections areprocessed in which stage of MUD.

The MUD Controller 400 takes the estimated symbols from the DemodulateUE 201, 202, 203 function associated with each MUD stage and passes themto the Link Manager 500. Additionally, the MUD Controller 400 estimatesthe SINR on the UE selected for cancellation in each MUD stage, andpasses that information to the Link Manager 500 for inclusion into thepower control loop processing. If no UE connections have an adequateSINR for the application of MUD, or if more UE connections can beprocessed than the number with adequate SINR, then the MUD Controller400 requests that the Link Manager 500 raise the target E_(b)/N₀ for theUE next on the sorted MAI list.

Each MUD Processor 301, 302 operates on those UE connections chosen bythe MUD Controller 400. Specifically, the MUD Processor 301, 302estimates UE connection signal characteristics, re-constructs a digitalbaseband replica of the signal, and subtracts the replica signal out ofeach antenna digital baseband signal 40 thereby removing the UEconnection's contribution to MAI. Because MUD Processor 301, 302performance often improves when applied successively to a group ofinterference sources, a second stage of MUD Processing 302 is includedin the receiver architecture. Additional successive stages are notincluded due to receiver latency constraints and current hardwareperformance abilities. Each successive MUD stage is followed by aDemodulate UE 202, 203 function, each of which require a discrete amountof time to provide the data required by the MUD Processor 301, 302.

Acquisition Processing 600 includes searching known UE acquisitionrequest codes to acquire new UE requesting service. The location ofAcquisition Processing 600 in the architecture reduces the MAI seen bythe receiver when searching for network access requests, an importantfeature because UE in CDMA networks successively increase the radiatedpower of access requests when such requests are not acknowledged by theserving base station. By minimizing MAI during acquisition processing,the receiver reduces the overall network MAI levels due to serviceaccess requests.

The STAP Covariance Inverse function 700 estimates a single covariancefunction for all of the UE connections associated with each MUD stage.Because the interference environment as detected by the receiver changesafter each stage of MUD, the STAP Covariance Inverse is updated aftereach stage to maximize STAP and MUD performance gain in light of latencyand processing (among other) constraints.

The Link Manager 500:

-   -   manages the inner and outer power control loops,    -   adjusts user operating points to support the requirements of the        MUD Controller 400, and    -   helps provision high data rate requests in a manner that is        consistent with the requirements of effective STAP and MUD MAI        reduction.

The Link Manager 500 leverages signal processing SINR gains intoincreased capacity by adjusting the power control commands to the UE inthe network. One effect of the advanced signal processing is to increasethe received SINR of the UE signals. In the absence of appropriate powercontrol, the result is simply decreased bit error rate and increasedlink reliability. However, by exploiting the improved interferencesuppression to reduce UE transmit power the interference posed by eachreduced-power UE to other UE is reduced in preferred embodiments of thepresent invention. As capacity is practically defined in these networkswhen the interference environment reaches a particular level, increasedcapacity results when power is controlled to keep reliability atpredetermined specified levels. For example, a 3 dB SINR gain on a givenUE supports a 3 dB reduction in radiated power by the UE, which whenapplied to all of the UEs in the network can result in a 4-8 dBreduction in MAI (depending on the QoS). FIG. 3 illustrates thereduction in MAI provided by STAP gain, and the amount of additionalnetwork capacity gained when such reductions in MAI are realized byimplementing STAP. For example, a 3 dB reduction in MAI results in anadditional 3000 kbps of capacity in a cell serving 384 kbps users.Looking at the left hand portion of FIG. 3, and the 384 kbps curve, thesystem would support about 8 more users per BS if the MAI reduction is 3dB. At 384 kbps/user, this amounts to about another 3000 kbps capacityper BS.

Capacity and Coverage Performance Summary

In order to illustrate the coverage and capacity benefits of amulti-stage STAP and MUD Receiver of the present invention, a WCDMA RFNetwork simulation is used. The simulation was originally developed tomodel Interim Standard (IS)-95 network performance, and has beenbenchmarked against existing IS-95 networks.

Overview of The Simulation. FIG. 4 illustrates the features and the flowof the RF network simulation. A network laydown 810 of users isgenerated which feeds several functions required to determine whether ornot the chosen laydown of users is supportable by the air interface.Propagation loss is computed 820 using the Hata model and is used todetermine UE-to-base-station assignments 830. In accordance with the3GPP WCDMA specification, each UE is characterized by a control channeland a mix of voice and data channels as commanded by input to thesimulation 840. Either a block inversion or an iterative technique isused to compute the network power control solution 850, which, in turn,is used to determine which connections in the network can and cannot beserviced 860. Network capacity for a laydown is computed by summing allserviced data channels across the network. In order to determine amaximum network capacity condition, users are continuously added to thenetwork until a network breakage condition is reached. The networkbreakage condition used to support this disclosure is when five percentof connections are denied service in the center cell in the network.FIG. 5 illustrates such a condition for a network of voice users. Theplot on the left illustrates a healthy operating network with an averageof 35 voice users per sector. The plot on the right illustrates thebreakage condition. When attempting to service an average of 70 usersper sector, 6.2% of the connections are denied service in the centercell, resulting in the declaration that the network has been driven tomaximum capacity. The I+N plots illustrate the level of MAI in dBmilliwatts (dBm) in the network at each sector. FIG. 6 illustrates threenetwork loading conditions for a mix of voice and data connections inthe network.

The WCDMA RF Network Simulation is used to compare network capacity forpreferred embodiments of the disclosed invention and a conventionalWCDMA receiver. For reference, a conventional WCDMA receiver is definedas a selection diversity based antenna followed by a rake filter and thede-spread, demodulate, and decode functions as described in the airinterface specifications of the 3GPP.

Simulated Performance of Preferred Embodiments of the DisclosedInvention. Table 1 summarizes the capacity advantage of embodiments ofthe invention over conventional WCDMA reverse link processing, asdetermined using the simulation, for the following baseline conditions:

3 sectors per cell;

4 antenna elements per sector; and

4 MUD processes per sector.

As will be illustrated later in this document, embodiments of theinvention are scalable. The 5× improvement operating point is chosen asan example. Note that the performance gain illustrated in Table 1 variesover QoS mix. The network laydown that has a significant number of highdata rate connections (384 kbps) shows the greatest benefit forembodiments of the invention due to multi-stage MUD processing.

One aspect of embodiments of the invention is the opportunity to provideincreased high capacity voice and data services over a macro-cellularnetwork footprint without resorting to microcell or picocell solutions.Because conventional WCDMA reverse link air interface processingtypically saturates at about 2 Mbps, equipment vendors offer microcelland picocell solutions to augment the macrocell sites when additionalnetwork capacity is required. This competing solution has cleardisadvantages to the network operator in network cost and complexity.FIGS. 7-9 illustrate that embodiments of the invention support highcapacity service over macrocell footprints. The radius of a typicalmacrocell is between one and three kilometers. FIGS. 7-9 illustrate thatcapacity deteriorates only a minor amount as the cell radius grows tothree kilometers.

The principal output of the WCDMA RF Network Simulation is a link bylink determination of whether the UE call is adequately serviced. Bydefining maximum capacity as the point at which no more than 5% of theusers in any one class of service are not adequately serviced, one mayexplore the network capacity, in terms of users (capacity) or throughput(aggregate data rate), for various quality of service mixes. This hasbeen done for both a conventional W-CDMA receiver and the one describedherein and compared in FIGS. 7-9.

FIG. 10 illustrates the number of additional micro sites required todeliver the same capacity over the network as preferred embodiments ofthe present invention. An increase of approximately 3.5 in the number ofsites is required to deliver the same capacity as preferred embodimentsof the invention. The results in FIG. 10 allow macrocell base stationcapacity at the microcell, which is unlikely to be available on thecommercial market. Manufacturers of microcell and picocell solutionstypically provide less than half of the throughput in these solutionsrelative to their macrocell solutions due to form factor constraints.Hence, the 3.5 multiplier is a conservative estimate and is likely togrow as networks are deployed.

FIG. 10 also illustrates the significant reduction in MAI provided bypreferred embodiments of the invention. The I+N plot on the leftillustrates that the MAI level at each sector in the network variesbetween approximately −94 and −96 dBm for the conventional WCDMAsolution. The middle plot illustrates excellent control of interferencefor embodiments of the invention.

TABLE 1 Reverse Link Solution Capacity and Throughput - 37 Cells inNetwork, 2 km Radius. Conventional WCDMA WCDMA Receiver Receiver withRLS Quality of Service Capacity:Throughput Capacity:Throughput IncreaseOver (Type: kbps) (UE/BS:kbps/BS) (UE/BS:kbps/BS) Conventional WCDMAVoice: 8 204.8:1638.4 847.1:6776.8 4.1x Data: 64  27:1728 119.9:7673.64.4x Data: 144  15:2160  68.9:9921.6 4.6x Data: 384  6:2304 35.9:13785.6 6.0x Data: 2048  1:2048   6:12288 6x   90% Voice: 8 30:1368  269.9:12273.6 9.0x 10% Data: 384 97.1% Voice: 8 105:1968525:9840 5x   2.9% Data: 384 90% Voice: 8 120:1632 480:6528 4x   10%Data: 64 66.7% Voice: 8  63:1680 252:6720 4x   33.3% Data: 64 50% Voice:8  48.0:1728.0 210:7560 4.4x 50% Data: 64

Scaling Capacity

Receiver architectures of the present invention offer the means to scalecapacity as a function of equipment cost. Although it provides a nominalof 5× of Base Station capacity improvement, the architecture can scalein the number of sector antennas supported at the BTS, and in the numberof MUD processes executed at the BTS, and can provide up to at least 20×capacity improvement over conventional WCDMA depending on the quality ofservice mix supported in the network.

The architecture facilitates a graduated cost (as measured incomputational complexity) versus benefit (as measured in additionalcapacity and/or coverage) tradeoff. The architecture is structured suchthat the mapping of performance to complexity is modular in the numberof parallel programmable processors. Data flow and latency requirementsare accommodated at each scaling level of the receiver.

The WCDMA receiver architecture combines the control logic and signalprocessing thereby enabling a network operator to selectively controlradio resources such that various mixes of capacity and coverageenhancements may be obtained.

Embodiments of the disclosed invention offer at least two scalingmechanisms. First, the number of antennas employed at the base stationlocation may be varied to influence STAP. As the number of antennas perbase station sector increases, SINR gain is realized due to array gain,which scales linearly with the number of antennas, and increasedinterference rejection is obtained due to the increased number of STAPDegrees of Freedom (DOFs). SINR gain supports lower power operatingpoints for UE which reduces the MAI for a given network loadingcondition.

A second scaling mechanism is the number of MUD processes performed.Additional medium (64 kbps) and high (384 kbps) rate UE connections canbe added to a sector if each new UE connection is supported by a MUDprocess. The air interface will saturate quickly if the addition of suchconnections is not supported by the addition of a MUD process peradditional UE connection. Referring to Table 1, a conventional WCDMAreceiver can support a maximum of two (2) 384 kbps UE connections perBTS sector. Because the addition of a third high rate UE connection in agiven sector is effectively denied by the BTS power control solution,the conventional receiver does not scale up in capacity. BTS equipmentvendors advertise product scalability, but typically such products scaledown, not up.

When employing preferred embodiments of the claimed invention, a networkoperator can tailor the number of antennas and MUD processes to eachsector. An additional degree of flexibility is the ability tore-allocate MUD processes sector to sector.

Table 2 illustrates the impact of each scaling mechanism, and thescalability of the integrated solution. Note that preferred embodimentsof the invention are alternately identified as WCDMA with RLS; where“RLS” corresponds to Reverse Link Solution. It should be noted that theresults in Table 2 represent the specific variant of MUD that preferredembodiments of the disclosed invention employ, and is not representativeof all MUD variants. The first two columns address the scalability ofMUD processing without STAP. Column 1 represents the baseline solution,four MUD processes per sector, and column 2 represents twice thebaseline, or eight MUD processes per sector. Note that, without STAP,there is little gain achieved by simply increasing the number of MUDprocesses. The following observations explain the lack of scalabilitywithout STAP:

-   -   MUD processing provides significant interference rejection when        used to cancel medium and high data rate connections (the MAI        contribution of a single voice user is minimal).    -   Without STAP, medium and high data rate connections interfere        with each other and do not support adequate SINR performance to        facilitate MUD. This may be avoided by employing joint decoding,        but the complexity of such a receiver grows exponentially in the        number of users.

The following design features of the disclosed embodiments of theinvention support the performance disclosed:

-   -   Apply STAP beamforming prior to the first stage of MUD        processing.    -   Re-compute new STAP beamforming weights prior to the second        stage of MUD Processing.

Referring again to Table 2, column 3 represents a baseline embodiment ofthe present invention, with approximately 5× improvement overconventional WCDMA processing. Column 4 illustrates the effect ofcanceling four (4) additional users per sector over the baseline, withmost of the capacity gain accruing in scenarios that include 384 kbpsconnections. Columns 5 and 6 illustrate the effect of doubling thenumber of antenna elements, which theoretically increases per user SINRby 3 dB.

As columns 1 and 2 in Table 2 illustrate, simply increasing the numberof MUD processes does not support additional data connections withoutSTAP gain. FIG. 13 illustrates the STAP SINR gain sufficient to supporta given number of MUD Processes. As illustrated, implementing ten MUDprocesses without a solution that provides at least 6 dB of STAP gainwill not provide increased network capacity. The figure was derived by,at each STAP gain level, increasing the number of MUD processes untilnetwork capacity no longer increases. For this figure, MUD is atwo-stage process.

TABLE 2 Reverse Link Solution Scalability - 37 Cells in Network, 2 kmRadius 1 Antenna 1 Antenna 4 Antenna 8 Antenna 8 Antenna Element,Element, 4 Antenna Elements, Elements, Elements, Elements, 4 MUDProcesses 8 MUD Processes 4 MUD Processes, 8 MUD Processes 4 MUDProcesses 8 MUD Processes Capacity: Capacity: Baseline Capacity:Capacity: Capacity: Quality of Service Throughput ThroughputCapacity:Throughput Throughput Throughput Throughput (Type: kbps)(UE/BS:kbps/BS) (UE/BS:kbps/BS) (UE/BS:kbps/BS) (UE/BS:kbps/BS)(UE/BS:kbps/BS) (UE/BS:kbps/BS) Voice: 8 215.2:1721.6 221.4:1771.2847.1:6776.8 859.2:6873.6 1658.5:13268.0 1674.4:13395.2 Data: 64 33:2112  36:2304 119.9:7673.6 125.8:8051.2  235.9:15097.6 241.8:15475.2 Data: 144  20.9:3009.6  21:3024  68.9:9921.6 74.9:10785.6  131.9:18993.6  138.0:19872.0 Data: 384  9:3456  9:3456 35.9:13785.6  39:14976  68.3:26227.2  72:27648 90% Voice: 8  30:1368 30:1368  269.9:12273.6  300.0:13680.0  480:21888  540:24624 10% Data:384 50% Voice: 8  59.9:2153.6  66:2376 210:7560 221.9:7985.6 413.3:14859.2  425.2:15284.8 50% Data: 64 Average Increase 1.2x 1.3x5.4x 5.8x 10.3x 10.9x Over WCDMA

The addition of antenna elements and/or MUD processes at the basestation impacts processing hardware complexity. To provide insight intothe processing complexity impact of STAP and MUD, FIG. 12 summarizes theprocessing complexity of the baseline of the claimed invention.Equipment cost has been found to be directly proportional to processingcomplexity.

Antenna System Diversity

The receiver architecture may be implemented in software and may be usedwith various types of base station antennas, and does not requireantenna calibration or special antenna maintenance. The solutionsupports various types of antenna mounting configurations, remoteantennas, as well as moving or swinging antennas with no appreciableperformance degradation.

The WCDMA receiver architecture employs a STAP algorithm that optimallycombines the signals from arbitrary antennas at arbitrary locations, isadaptive, and is trained on UE transmissions and thereby providing asystem that is self-calibrating.

Preferred embodiments of the invention, because they are digital signalprocessing solutions, do not levy special antenna requirements on theoverall base station solution. The number of and placement of antennasis not critical to implementing the solution, although they influencethe obtainable interference mitigation. For example, an antennaconfiguration optimized for a selection-diversity-based receiver iseasily supported by preferred embodiments of the invention, subject tothe digital receive chain requirements described earlier. There are STAPperformance gain limitations described earlier that relate to the numberof antenna elements, but the solution can be implemented for any numberof antenna elements.

Because they are digital signal processing solutions, preferredembodiments of the invention can be implemented in software ongeneral-purpose computing hardware or digital signal processing (DSP)hardware solutions. It is possible that a manufactured product basedupon the claimed invention would include special purpose computinghardware to reduce manufacturing costs, but the solution does notrequire such hardware.

One advantage of preferred embodiments of the disclosed invention is thelack of a requirement for antenna array calibration. Because thoseembodiments do not require estimation of the angle of arrival of the RFenergy from UE, computation and maintenance of complex calibrationtables is not required. A steering vector is estimated for each UE bycorrelating received baseband quadrature data with the known scramblingcode for each UE. If angle of arrival information were required, acalibration vector would likely be needed to convert the estimatedsteering vector into a spatial angle relative to the antenna array.Generation of calibration vectors periodically requires an extensive setof field measurements, and is difficult in an urban environment. Thefact that preferred embodiments of the present invention do not requiregeneration of calibration vectors significantly reduces the amount ofengineering touch labor required to operate base station equipment in anurban environment.

Interleaved STAP and MUD

This integrated WCDMA receiver architecture offers a multi-stage filterarchitecture that interleaves layers of MUD and STAP. Preferredembodiments of the invention tailor the available STAP gain to each linkdepending on whether it will be specifically used by the MUD processor.Prior to a link's cancellation by the MUD processor, STAP gain is usedto improve the accuracy of channel estimation for links to be handled bythe MUD processor. Subsequently, STAP gain is reallocated to maximizingSINR of the remaining links in the absence of the cancelled link'sinterference. It is advantageous that a WCDMA receiver designed toincrease air interface capacity employ a combination of STAP and MUD forthe following reasons:

-   -   STAP solutions work best when applied to RF environments in        which the number of interference sources is less than the number        of array elements employed. Typical WCDMA networks are likely to        have 10-200 sources active per sector. The benefit of employing        more than eight antenna array elements in a given base station        sector is unlikely to justify the associated equipment and        processing costs in WCDMA networks. STAP is an optimal        beamforming solution in a time-invariant channel. It is        generally much preferred to alternatives such as the Maximum        Ratio Combiner (MRC). However, in a loaded WCDMA network it will        be employed under sub-optimal interference conditions in which        the STAP solution is under-determined. Under those conditions a        STAP solution will not deliver the 20-30 dB SINR improvement        possible when the interference subspace is properly matched to        the array rank. In anticipated WCDMA interference environments,        STAP will deliver SINR gain on the order of 10*Log 10(n), where        n is the number of antenna elements.    -   Each stage of MUD reduces the rank of the STAP interference        sub-space observed at the antenna array, and increases the        effectiveness of each subsequent stage of STAP. Of the 10-200        sources active per sector, MAI is dominated by the data        connections that require higher transmit power levels to        compensate for less spreading gain at the receiver. Each        successful stage of MUD eliminates some number of data        connections from the interference sub-space, supporting a        re-calculation of the now lower rank STAP covariance matrix. The        re-calculation has two benefits. First, it will provide        increased STAP gain on the remaining data connections that must        be processed in the subsequent stage of MUD. Second, it may        provide increased STAP gain on the voice connections.    -   A maximum of a single MUD process per sector is supported at a        base station that does not employ STAP. Therefore, an        exclusively MUD-based solution is not scalable, in the manner        disclosed herein, without STAP beamforming. A joint MUD is        scalable but, because it scales exponentially in computational        complexity it is not feasible for cost effective implementation.        It is also possible to use power control to stagger the        operating points of the data connections which supports many        stages of successive interference cancellation. However, the        preferred embodiments described herein recommend only two (2)        MUD stages, due to receiver latency requirements and current        commercially available hardware platforms.

Referring to FIGS. 1 and 2, the STAP and MUD filter architecture 100illustrated in FIG. 1 employs two stages of MUD Processing 301, 302, andthree STAP beamforming solutions 210, each associated with a DemodulateUE 201, 202, 203 function. The first two STAP beamforming solutions 210are only applied to the connections assigned to MUD processing by theMUD Controller 400. The third STAP solution 210 is applied to all of theremaining connections in the sector. If MAI is reduced by 1 dB for allusers in a sector, all the users may reduce their radiated power by 1 dBand still maintain an adequate SINR margin in the receiver for reliabledemodulation. This would save UE battery life. Alternatively, thenetwork could support additional users if UE maintained emitted powerlevels. This effect is demonstrated in the left hand panel of FIG. 3.

STAP Filter Design

Realizable STAP filters typically use computational complexity reductiontechniques, such as Least Mean Square (LMS) or Root Least Square (RLS)estimation techniques, to solve for filter weights. These estimationtechniques, although prominent in current fielded STAP applications,have at least two disadvantages relative to the disclosed invention.First, in order to support the MUD processing approach advocated herein,the Link Manager 400 introduces UE connection power variations and radiochannel occupancy variations that have a deleterious effect on weightsestimation techniques that have convergence lag times (such as LMS andRLS). Second, a separate LMS estimation is required per user, which,when applied to a scenario with large numbers of users (>50), does notprovide computational savings over the technique advocated herein.

The STAP Filter design for preferred embodiments of the presentinvention leverages a block adaptive, global calculation and inversionof the interference covariance matrix, with a per-UE-connectionestimation of a steering vector. For the purpose of this disclosure, thesteering vector is also block adaptive. One advantage of the blockadaptive approach is that it utilizes a discrete number of data samples,i.e., a block, to produce a single statistical estimate of thecovariance which automatically captures all sub-space activity in thegiven block. When contiguous blocks of data are used to estimate thecovariance—for instance, consider a block a single slot of WCDMAdata—all signal environment phenomena are captured. In the context ofthe disclosed invention, such a computationally complex option isafforded because a single global covariance estimate and inversesuffices for all UE connections.

Block adaptive calculation of the STAP interference is represented bythe following equation,

$\begin{matrix}{\hat{R} = {\frac{1}{K}X^{H}X}} & (1)\end{matrix}$where

$\begin{matrix}{{X^{H} = \begin{bmatrix}{{x_{1}\left( n_{1} \right)},{x_{1}\left( n_{2} \right)},\ldots\mspace{14mu},{x_{1}\left( n_{K} \right)}} \\{{x_{2}\left( n_{1} \right)},{x_{2}\left( n_{2} \right)},\ldots\mspace{14mu},{x_{2}\left( n_{K} \right)}} \\\vdots \\{{x_{M}\left( n_{1} \right)},{x_{M}\left( n_{2} \right)},\ldots\mspace{14mu},{x_{M}\left( n_{K} \right)}}\end{bmatrix}},} & (2)\end{matrix}$and where K is the number of samples in the block estimate.

Computation of the steering vector v for a given UE connection requirescorrelating the digital baseband data with the known scrambling code onthe UE pilot channel and is accomplished by implementation of thefollowing equation,v=

X|d*

  (3).

The global interference covariance and the UE-specific steering vectorare used to calculate the STAP beamformer weight vector for a given UEconnection using the following equation,w=R ⁻¹ v  (4).

As explained above, application of the STAP beamformer weights convertsan m-channel data stream 40 into a single data stream of digitalbaseband data 212 that is de-scrambled and de-spread 220, de-interleaved230, and finally decoded 240 using the scrambling code, spreading andchannelization codes, interleaving technique, and error correctioncoding specified by wide-band standard committees such as the 3GPP.

Because the interference subspace in the covariance matrix is alteredafter each stage of MUD Processing 301, 302, a new global interferencecovariance is calculated to support STAP beamformer weights calculationafter each stage of MUD Processing 301, 302.

MUD Filter Design

There are a wide variety of MUD techniques that have been disclosed inthe prior art. Optimal detectors have been developed using both maximuma posteriori detection (MAP), and maximum likelihood sequence detection(MLSD). S. Verdu, “Minimum Probablily of Error for Asynchronous GaussianMultiple-Access Channels”, IEEE Transactions on Information Theory, vol.IT-32, no. 1, January 1986, pp 85-96]. Both of these detectors, whileachieving optimal performance, are far too complex to be efficientlyimplemented in a practical CDMA system. These techniques have been shownto have a computational complexity that is exponential in the number ofusers, and they require accurate phase and amplitude estimates. S.Verdu, “Adaptive Multi-User Detection”, Code Division Multiple AccessCommunications, S. G. Glisic and P. A. Leppanen, Eds., pp. 97-116, TheNetherlands, Kluwer, 1995.

One thrust of recent research in this area has been the development ofsub-optimal MUD techniques with lower complexity. These techniques maybe broadly divided into linear detectors and subtractive interferencecancellation detectors.

Linear Detectors. The linear detectors generally require an inversion ofa correlation matrix that is on the order of the product of the numberof UE connections and the message length, and are still quitecomputationally complex for a large number of UE connections andreasonable message lengths.

Subtractive Interference Cancellers. Subtractive interferencecancellation techniques involve the demodulation, symbol estimation,re-modulation and subtraction of each UE signals. These techniques canbe further divided into serial and parallel implementations, paralleland serial.

Parallel Subtractive Interference Canceller Implementations. Theparallel implementations typically require a minimum of 2 stages, wherethe tentative data decisions for each of the N users are re-modulatedand subtracted from N−1 data streams for a second estimation.

Serial SIC. Serial SIC (also referred to as Successive InterferenceCancellation), where UE connections from the largest MAI contribution tothe smallest, are sequentially estimated and subtracted, performs nearlyas well in many circumstances, but results in a long latency for a largenumber of UE connections if it is applied to every UE. The UEconnections processed in the first stage of SIC get no benefit from theMUD processing, but the system capacity improves, and the MAIenvironment, as seen by subsequent stages, from all of the other UEconnections is reduced. Serial SIC is roughly half as computationallycomplex for a given number of UE connections as parallel interferencecancellation, where two stages are employed.

For a multi-rate CDMA system like WCDMA, group-wise serial interferencecancellation is an efficient technique, providing a compromise betweenperformance and computational complexity. In this technique, all UEconnections with similar MAI contributions are processed in a singlestage, and the stages start with the largest MAI contributors. Each bitdecision is made independently, but the processing at each stage has thebenefit of the MAI reduction of the previous. Thus, preferredembodiments of the current invention start with the greatest MAIcontributors to provide greater benefit to the subsequent processing.

The strategy utilized in preferred embodiments of the disclosed MUDProcessor 301, 302 is somewhat different than approaches employed inother approaches. In a multi-rate service, such as WCDMA, there areclasses of UE connections that disproportionately contribute MAI,specifically those with a low spreading factor. Recognizing thecomputational complexity of MUD, preferred embodiments of the disclosedinvention processes those UE connections first. Additionally, in anetwork where the majority of UE connections are still voice, the UEs ina sector will vastly outnumber the number of available MUD processes. Inthis case, little additional benefit is derived by canceling the highspeed data user to MAI level below that contributed by a voice user.Hence the strategy utilized in the MUD Processor 301, 302 is to applyMUD to as many data connections as possible, and cancel theseconnections down to the ambient MAI level of the voice users in thenetwork.

The MUD Processor 301, 302 operates on entire UE connections, instead ofoperating on single UE connection channels. The MUD Controller 400selects and assigns UE connections as MUD cancellation candidates, andthe MUD Processor 301, 302 estimates, re-constructs, and subtracts thecontrol and data channels associated with the cancellation candidate.The MUD Processor algorithm is illustrated in FIG. 13. For a givencancellation candidate, a minimum mean square error estimate is made fora linear filter that best reproduces the received signal at each antennafrom the UE. The filter estimate w is obtained by correlating thereceived signal to the UE control channel pilot bits. Each active UEDPCH is then re-constructed by applying w and a power scaling factor tothe estimated bits for the DPCCH and each DPDCH 1320, 1330. There-constructed channels are summed and subtracted from the associatedantenna digital signal stream to effectively eliminate signal energyfrom the candidate UE from the signal stream. As stated above, the MUDProcessor 301, 302 is designed to cancel UE data channels down to theambient MAI level of the UE voice channels in the network, typically 15dB of cancellation is desired. The number of time taps in the MUD filteris adjusted to this requirement.

Stated in another fashion, FIG. 13 illustrates a preferred embodiment ofthe MUD processing algorithm for one UE. Using the received baseband,oversampled data, d, and the known chip sequence for the pilot bits, c,on the DPCCH, a minimum mean square estimate is made of a set ofweights, w_(mk) 1310. These weights, when applied to a sliding window(length L+1) of chips transmitted from the UE, x, reconstruct theportion of the received signal, y, due to that UE DPCH for each antennaelement and sub-sample. The factor b is the known power ratio betweenthe DPDCH and the DPCCH.

Multi-Stage Filter Architecture Performance

To illustrate the SINR improvement of the disclosed embodiments of thepresent invention over conventional WCDMA processing a WDCMA airinterface link simulation was developed. The simulation uses the networkconnection laydowns and associated power control solutions generated inthe WCDMA RF Network Simulation presented earlier. The WCDMA linksimulation produces digital complex baseband data for each BTS antennaover an input number of data frames, and includes real world effectssuch as the transmit filter shape, multipath, and Doppler. It is fullycompliant with the 3GPP WCDMA specification. FIG. 14 illustrates therelationship between the WCDMA RF Network Simulation and the WCDMA LinkSimulation.

A Matlab® implementation of the claimed invention is used to process thesimulation data and compute performance metrics. A similar Matlab®implementation of a conventional WCDMA processor is used to provide abasis for improvement claims. The following steps are employed tosubstantiate link level performance of the claimed invention. Run theWCDMA RF Network Simulation to a maximum capacity that includes themodeled STAP and MUD gains for a given network QoS mix. Use the UEconnection laydown and associated power control solution in a selectedBTS sector in the RF Network Simulation to generate WCDMA LinkSimulation data. Process the WCDMA Link Simulation data with the Matlab®implementation of a conventional WCDMA processor and the Matlab®implementation of the claimed invention. For the conventional and theclaimed invention solutions, compute the SINR for each UE connectionchannel and form a histogram of the SINR set. Compare the histograms tothe required SINR operating points of each channel type.

The histogram of UE connection channel SINRs aligns with the 3GPPspecified channel operating point. The histogram of SINRs for theconventional processor falls short of the specified channel operatingpoint by the number of dBs of SINR improvement that the claimedinvention realizes. Furthermore, the difference between the means of thetwo histograms represents the actual SINR improvement of the claimedinvention.

FIG. 15 illustrates the approach described above for a networkcomprising only UE voice and control channels. Note that the histogramfor preferred embodiments of the invention aligns with the operatingpoint for WCDMA voice channels, and provides 6.2 dB of gain over theconventional processor.

FIG. 16 illustrates the results for a network comprising a mix of voiceand high rate (384 kbps) UE channels. Note that the histograms forpreferred embodiments of the invention align with the operating point ofvoice and high rate data connections, and that 6.9 dB of gain for voiceusers and 6.3 dB of gain for data users is realized over theconventional processor in both cases.

MUD Processing on Pre-Decoded FEC Symbols

A WCDMA receiver architecture of the present invention offers a filterarchitecture that performs MUD processing on the received forward errorcorrection symbols, prior to the de-interleaving and decoding of thesesymbols to data bits to support no greater than 30 msec of latencythrough the receiver

The architecture of the claimed invention is structured to introduce nomore than 30 msec of receiver processing latency, from receipt ofdigital data from the antennas, to multiplexing and shipping outtransport channels of information bits. Because forward error correctiondecoding adds between 20 and 80 msec of latency to receiver processing,and because the multi-stage receiver also introduces processing latency,it is advantageous to estimate symbols for the MUD Processor beforedecoding the forward error correction code.

Symbol errors have a deleterious effect on the performance of the MUDProcessor 301, 302. In the absence of coding gain, to insure sufficientsymbol error rates on the UE connections undergoing MUD processing, theLink Manager 500 must command increased operating power for theseconnections.

Performing MUD processing on pre-decoded FEC symbols has two favorabledesign effects. First, the UE connections undergoing MUD processing willhave very low symbol error rates after error correction decoding.Second, very computationally complex coding schemes, such as TurboCoding, are not required by the disclosed invention.

Reducing Power Required to Establish a Network Connection

The disclosed integrated WCDMA receiver architecture offers amulti-stage filter architecture that minimizes the power required toestablish a network connection on the random access channel by a givenuser, thus minimizing the network interference associated with networkaccess requests.

UE acquisition channel requests have the potential to contribute MAI ina loaded WCDMA network. Because the claimed invention does not apply MUDprocessing to network UE acquisition channels, and because the UEacquisition process repeatedly increases UE acquisition channel poweruntil a serving BTS responds, it is important to minimize the MAIbackground when performing acquisition processing. Preferred embodimentsof the present invention employ an approach to acquisition processingthat allows UE acquisition channel radiated power to not exceed that ofa typical UE voice channel.

Specifically, Acquisition Processing 600 is placed after the last stageof MUD Processing 302. Performing acquisition processing at thislocation in the receiver reduces the MAI background in which acquisitioncorrelations are performed. Because MAI is reduced at this point,relative to a conventional receiver for a given acquisition correlationwindow length, UE access requests will require significantly lessradiated power when processed by preferred embodiments of the presentinvention implementing this approach, thereby reducing the MAIcontribution of the access request.

Applying a Single MUD Process to Several User Equipment Transmissions

Another method of the present invention involves applying a single MUDprocess simultaneously to several UEs, thereby providing reduced networkinterference for reduced computational complexity.

Due to the computational complexity of a MUD process, a significantincrease in performance is achieved with no additional complexity if asingle MUD process is applied to several users. The 3GPP WDCMA airinterface specification does not preclude such an approach.

Preferred embodiments of the present invention provide four (4) MUDprocesses per BTS sector, with the MUD processes allocated over bothstages of MUD by the MUD Controller 400. For a QoS mix in the sector ofinterest that comprises four (4) 384 kbps UE connections with theremaining UE connections providing voice service, the claimedarchitecture works very well. The four (4) high data rate UE connectionsare cancelled by the MUD Processor 301, 302, and voice capacity isunaffected by the high data rate connections and enhanced by STAP gain.

Consider a QoS mix in a sector with thirty (30) 64 kbps UE connectionsand the remainder of the UE connections servicing voice channels.Although preferred embodiments of the present invention are scalable inMUD processes, each MUD process impacts the hardware cost of the overallsolution. Rather than scaling to thirty (30) MUD processes for thesubject QoS mix, an innovative scheme that is consistent with the 3GPPWCDMA specifications is offered. An example of the innovation is to makethe thirty (30) 64 kbps UE connections look like five (5) 384 kbpsconnections. This is accomplished by time multiplexing 384 kbps channelsamong the thirty (30) UE connections. Specifically, in a given frame,five (5) 384 kbps connections are established among the thirty (30) UEconnections. On a subsequent frame, the previous five (5) 384 kbps UEconnections are torn down and five (5) new 384 kbps UE connections areestablished. In effect, the UE requesting 64 kbps is receiving 384 kbpsevery sixth frame, providing an effective bandwidth of 64 kpbs.

The MUD Controller 400 and Link Manager 500 logic support such a scheme,and the MUD Processor 301, 302 only requires five (5), rather thanthirty (30), MUD processes to cancel the MAI from the thirty (30)intermittent connections down to the ambient voice MAI level. Table 4illustrates the capacity increase associated with this innovation. Notethat the performance cited in Table 1 did not include this innovation.

TABLE 4 Reverse Link Solution Using Time Multiplexing - 37 Cells inNetwork, 2 km Radius WCDMA Receiver RLS Using Time- with RLS MultiplexTechnique Quality of Service Capacity:Throughput Capacity:ThroughputIncrease Over RLS (Type: kbps) (UE/BS:kbps/BS) (UE/BS:kbps/BS) (Type:UE/BS) Data: 64 119.9:7673.6 153.0:9790.1 Data: 33 50% Voice: 8 210:7560270.0:9719.0 Voice: 30 50% Data: 64 Data: 30 66.7% Voice: 8 252:6720360.0:9599.3 Voice: 72 33.3% Data: 64 Data: 36

It should also be noted that the STAP and MUD filters specified herein,because they are slot block adaptive, suffer no performance degradationdue to the increased time variability of the RF environment associatedwith this invention.

Single STAP Covariance Per Stage of MUD

Preferred embodiments of the invention include a technique for computinga single STAP interference covariance for all users per stage of MUD.

Reduced complexity implementations of STAP, such as LMS and RLSestimation, compute a new STAP weight vector once per signal sample foreach UE connection. These techniques effectively include the STAPinterference covariance calculation in the weights estimate, and do notrequire an explicit covariance estimate. Because LMS and RLS techniquesare iterative approaches, they require a discrete amount of weightsettling time, and suffer performance degradation in complex, highlyvariable RF signal environments.

Due the to anticipated degree of time variability in the WCDMA RF signalenvironment, and due to an economy of scale realized by having tosupport large numbers of UE connections, preferred embodiments of thepresent invention employ a global block adaptive approach to STAP weightestimation. The estimation is global because one estimate applies to allusers serviced in the BTS sector. Referring to FIG. 1, the globalcovariance estimate is inverted and sent to Demodulate UE 201, 202, 203to facilitate STAP weight calculation and application.

One benefit of the global block adaptive approach is that the STAPweights estimated over a data block automatically reflect any new RFenvironment conditions that occur within the time interval representedby the block.

Selecting Connections for MUD Processing

A novel technique is disclosed for selecting the network connections todedicate to MUD processing that reduces MAI at the receiver. This methodmeasures, tracks, and considers in-cell connections and users in softhandoff with the cell in the MUD processing selection process.

Significant increases in WCDMA receiver performance vs. complexity arerealizable by intelligent, real-time, allocation of MUD processes to UEconnections in the network. Selection of the most promising UEconnections increases the effectiveness of MAI reduction. Allocation ofMUD processes to poorly chosen UE connections will have a deleteriouseffects on network performance. Preferred embodiments of the presentinvention utilize a MUD Controller 400 to monitor performance, and toseek effective allocation of MUD processes to UE connections.

The MUD Controller 400 identifies UE connections to apply MUD processingto based on the MAI contribution of that UE, and whether the SINR on theUE connection is adequate to achieve the desired cancellation. It isdesirable in the context of preferred embodiments of the presentinvention to assure that a MUD candidate has adequate SINR to supportMUD Processing. The consequences of attempting to cancel a UE channelwith inadequate SINR is an increase in the MAI contribution of the user,and a decrease in overall network capacity. The dependence on SINR ofMUD processing is further complicated by the long latency associatedwith decoding of the forward error correction codes and interleavingspecified in WCDMA. These latencies range from 10 to 80 ms, and are fartoo long to tolerate prior to the MUD processing. Therefore, MUDprocessing is performed on the FEC symbols rather than the transportchannel information bits, and the benefits of forward error correctionprocessing will be transparent to the MUD Processor 301, 302 and MUDController 400.

UE MAI Calculation

One observable that is utilized in the MUD Controller 400 is the MAIcontribution of each individual UE. This is calculated from networkcommanded quantities to the extent possible for latency andcomputational complexity advantages. The MAI contribution of each UE iscalculated using equation 5.

$\begin{matrix}{{MAI} = {{10{{Log}_{10}\left( \frac{E_{b}}{N_{0}} \right)}_{Target}} - {10{{Log}_{10}({SF})}} + \Delta}} & (5)\end{matrix}$

In Equation 5, (E_(b)/N₀)_(Target) is the target energy per bit (FECsymbol in this case) commanded by the Outer Loop Power ControlAlgorithm, SF is the spreading factor, and A is the difference betweenthe (E_(b)/N₀)_(Target) and the measured (E_(b)/N₀)_(D) for the lastslot. Alternatively, the (E_(b)/N₀)_(D) for each UE connection could bemeasured as in the First Inner Power Control Loop, if the processingresources are available. The difference is less than the power controlloop step size specified as 0.5 or 1.0 dB by the 3GPP.

The MUD Controller 400 receives a UE table from the Link Manager 500that provides the data required to calculate the MAI contribution perUE. The UE table includes: UE identification number, scrambling code,channel configurations (number of channels, relative gain), OVSF codesin operation, commanded transmit power, and (E_(b)/N₀)_(Target) for eachUE in the sector.

UE SINR Requirement Calculation

The effectiveness of MUD processing is a function of the SINR of thesignal to be cancelled. MUD processing on UE whose E_(b)/N₀ is too lowcan potentially increase the MAI contribution of that user 6 dB, andreduce network capacity. Quantifying the limits on MUD candidacy istherefore important.

Processes that influence the performance of subtractive MUD cancellationinclude: the accuracy of the symbol estimates, and the accuracy of thechannel estimates. Table 5 shows simulated results of the minimum meansquare channel estimation performance as a function of chip Eb/N0 (theratio of the symbol Eb/N0 to the spreading factor). This cancellationperformance is substantially linear over the range of interest.

Chip E_(b)/N₀ Cancellation  −5 dB −22 dB −10 dB −17 dB −15 dB −12 dB −20dB  −8 dB −25 dB  −3 dBTable 5. MMSE Cancellation Performance as a Function of Chip E_(b)/N₀,Assuming No Symbol Errors.

A simple linear fit to this data yields the following.

$\begin{matrix}{{MMSE} = {{{- 0.92}\left( {{10{{Log}_{10}\left( \frac{E_{b}}{N_{0}} \right)}} - {{Log}_{10}({SF})}} \right)} - 26.3}} & (6)\end{matrix}$where MMSE is the cancellation performance assuming perfect symbols indB, Eb/N0 is the energy per symbol in dB, and SF is the spreadingfactor.

The error rate for the symbols is also a function of the signal tonoise. The impact of a symbol error on the cancellation performance isthat every chip associated with that bit will be of the wrong sign, sothat instead of canceling the signal, the signal amplitude is doubled.Doubling the signal amplitude quadruples the power of the UE's MAIcontribution. The impact of a symbol error is also a function of thespreading factor.

The probability of symbol error for a QPSK signal in additive whiteGaussian noise (AWGN) is given by,

$\begin{matrix}{{SER} = {0.5{{erfc}\left( \sqrt{\frac{E_{b}}{N_{o}}} \right)}}} & (7)\end{matrix}$

Each symbol error will result in SF chip errors, and the doubling of thesignal for the duration of those chips. The expected impact oncancellation is given by,

$\begin{matrix}{{Symbol\_ cancel} = {{2 \cdot {SF} \cdot {SER}} = {{SF} \cdot {{erfc}\left( \sqrt{\frac{E_{b}}{N_{o}}} \right)}}}} & (8)\end{matrix}$

Assuming that it is impractical to apply MUD to every user in thesector, and that the network has more voice users than data users,little network performance was gained for cancellation greater thanabout 15 dB. This results in the MAI contribution of a high rate datauser (a single channel with a spreading factor of 4) being roughly thatof a voice user. For a cancellation ratio of 15 dB, the requiredE_(b)/N₀ was calculated, considering both components. The results aregiven in Table 6.

TABLE 6 E_(b)/N₀ requirements to achieve 15 dB average cancellation Pre-Post- Cancellation Cancellation MAI MAI SER Contribution ContributionRequired Con- MMSE (dB relative (dB relative SF E_(b)/N₀ dB tributionContribution to noise) to noise) 4 3.20 −15.7 −23.7 −2.8 −17.8 8 4.35−16.1 −22.0 −4.7 −19.7 16 5.30 −16.6 −20.1 −6.7 −21.7 32 6.17 −17.8−18.1 −8.9 −23.9 64 7.10 −21.2 −16.2 −11 −26.0 128 8.90 −39.6 −15.1−12.2 −27.2 256 11.80 −100.0 −15.0 −12.3 −27.3

For spreading factors between 4 and 32, the contribution of the SERcomponent is dominant. For spreading factors between 64 and 256, theMMSE channel estimate errors are dominant. The MAI contribution of avoice user at 8 kbps with a spreading factor of 256, a target E_(b)/N₀of 7 dB and no cancellation is −17.1 dB relative to thermal noise.

The MAI contribution of a symbol error is quantized, and the signal tobe cancelled is instead doubled, causing a 6 dB rise in its MAIcontribution for the duration of the symbol. Assuming perfect powercontrol, the MAI contribution caused by a bit error can be calculated asfollows

$\begin{matrix}{{{MAI\_ Symbol}{\_ error}} = {{10{{Log}_{10}\left( \sqrt{\frac{E_{b}}{N_{o}}} \right)}} - {10 \cdot {{Log}_{10}({SF})}} + {6\mspace{14mu}{dB}}}} & (9)\end{matrix}$

Table 7 shows the MAI contributions as a function of spreading factor,and their duration and noise floor impact on a network whoseinterference level is 5 dB about the thermal noise. (Typically about 75%of poll capacity for conventional CDMA networks).

TABLE 7 Bit Error Impact for the E_(b)/N₀ level from Table 2 MAIDuration, Average Instantaneous Contribution, dB per Number of NetworkNoise SF relative to N event, μs Events per Slot Floor Impact, dB 4 3.21.0 13.1 1.8 8 1.3 2.1 3.1 1.2 16 −0.7 4.2 0.74 0.8 32 −2.9 8.3 0.16 0.564 −5.0 16.7 0.03 0.3 128 −6.2 33.3 0.008 0.3 256 −6.2 66.7 1e−7 0.3

The network noise floor impact of a single bit error for a UE with aspreading factor of 4 is 1.8 dB, which will have a significant impact oncapacity. However, the duration of this interference totals about 13.6μs out of a 667 μs slot so on the average the contribution per slot is−14 dB relative to thermal noise.

The interleaver will effectively scramble the burst errors caused by thepulsating noise floor, but the impact at this level is stillsignificant. Table 8 shows the impact on a second UE with a spreadingfactor of 4 operating in the sector with an SINR of 3.2 dB, assuming theR=½, K=9 convolutional encoder, and one of each class of user. The burstnoise caused by the errors in the symbol estimation process dominatesthe BER performance for spreading factors less than 16.

TABLE 8 Bit Error Impact for the E_(b)/N₀ level from table 2 on UE withSF = 4 Average Number Instantaneous E_(b)/N₀ of bits effected NetworkNoise during SF per Slot Floor Impact, dB event Aggregate BER 4 13.1 1.81.37 34.1e−5 8 6.3 1.2 1.93 6.5e−5 16 3.0 0.8 2.37 3.9e−5 32 1.3 0.52.67 3.6e−5 64 0.44 0.3 2.87 3.5e−5 128 0.026 0.3 2.94 3.5e−5 256 8e−60.3 2.94 3.5e−5

Additional margin needs to be added to accommodate multiple users ofeach class. However, the contribution of the users decreases withincreasing spreading factor. The resulting target symbol E_(b)/N₀ levelsare given in table 9.

TABLE 9 Target E_(b)/N₀ for MUD Processing Required E_(b)/N₀Targets forSF MUD 4 4.7 8 5.4 16 5.8 32 6.2 64 7.1 128 8.9 256 11.8MUD Controller Algorithm

Upon the initialization of a MUD Processor 301, 302 for a slot of data,it gets a UE table from the Link Manager 500 that includes the followingdata for each active UE in the cell and in soft hand-off with the cell:UE ID code, scrambling code, channel configurations (number of channels,relative gain), OVSF codes in operation, commanded transmit power,E_(b)/N₀ target, UE distance from base-station (or path loss estimate).

If a UE has a target E_(b)/N₀ that exceeds the thresholds given in table9, then the MAI contribution of each such UE is calculated usingEquation 5. A candidate UE table will be generated, and sorted by MAI.The number of candidate UE will be compared to the MUD processingresources for the sector. The candidates will be sorted into two groups.The group with the largest MAI contribution will be processed in thefirst stage of MUD processing and the remained will be processed in thesecond stage. The goal will be to have less than 3 dB spread in the MAIcontributions of the candidates processed in the first stage.

If none of the UE exceeds the threshold in Table 9, then MUD processingis not performed on this slot of data in preferred embodiments of thepresent invention. In this case, if any high or medium data rate usersare active (e.g. spreading factor<=16), then a request is made to theLink Manager 500 to increase the target E_(b)/N₀ of the UE with thelowest spreading factor. If more than one UE is utilizing this spreadingfactor, a preference is given to the UE closest to the BTS. This may bemeasured from E-911 location data if it is available, or approximatedusing a path loss estimate. This path loss estimate can be the ratio ofthe commanded transmit power level of the UE to the measured receivepower level of the UE. Since out-of-cell users who are not in softhand-off have an arbitrary time offset relative to the base station (andgiven that WCDMA is an asynchronous specification) finding these usersmay require a very large search of code space. Therefore, increasing thetransmit power of UE closest to the base station will help to mitigateout of cell interference. If no high data rate users are available, thenthe MUD Controller 400 requests that the Link Manager 500 set up anumber of lower data rate users to be multiplexed onto a single MUDprocess.

The first group of candidate channel entries is sent to the first MUDProcessor 301. The second group of candidate channel entries is sent tothe second MUD Processor 302. The received E_(b)/N₀ for the cancelledusers is estimated and passed to the Link Manager 500, and the estimatedsymbols are retrieved from the MUD Processor 302 and passed to the LinkManager 500.

Multiple Stages of MUD Processing

In another aspect, embodiments of the present invention optimallydistribute parallel MUD processing over two stages of MUD to provideimproved cancellation of selected MUD candidates.

A preferred implementation utilizes two stages of group-wise serial SICto reduce the MAI contributions of high data rate UE connections. OtherMUD techniques may be utilized (i.e., PIC or linear techniques),depending upon the available computational resources. However, greatersystem capacity gains are realized by applying many mediocre MUDprocesses than a few very good ones. Results of a nineteen-cell networksimulation are shown in FIG. 17 for a fully serial processor, asingle-stage parallel processor (where data decisions are madeindependently on each user in a single stage) and the two-stage parallelprocessor. There are approximately 13 UE per sector, each at 384 kbps,and the cell radius is 2 km. This simulation assumes 6 dB of STAPprocessing gain, and that there are processing resources to apply MUD tofour UE in each sector. The two-stage processor buys back much of theperformance of the fully serial processor.

If the overall spread in these UE's MAI contributions is greater than 3dB, then the UE whose MAI contributions range from the maximum to 3 dBless than the maximum will be processed in the first stage, and theremainder of the candidates will be processed in the second stage. Ifthe spread in the MAI contributions of the users is less than 3 dB, theywill be split evenly between the two stages. If the MAI spread among theUE to be processed is greater than 3 dB, the first round is selected tolimit that spread to 2-3 dB. The effectiveness of the channel estimationprocess is reduced for the higher spreading gains, since the channel isestimated on per chip basis, and the higher spreading factors result inless energy per chip. These channels would benefit from having at leastone stage of MUD before them, and the 3 dB bound was chosen because thespreading factors are incremented in powers of two. In this way, thehighest power UE connections are cancelled first, causing the greatestbenefit to the remaining users. For a system with limited processingresources (i.e., all practicable systems) this will efficiently applythe available resources.

Estimating the Amount of Desired MUD Cancellation

In further preferred embodiments, the invention estimates an amount ofcancellation on each MUD candidate to support efficient link management,and to ensure adequate cancellation of each candidate.

Preferred embodiments of the MUD Processor 301, 302 use UE transmissionsymbols before removal of forward error correction encoding instead ofdecoded symbols to save 10-80 msec of processing latency. A veryaccurate measure of MUD Processor 301, 302 performance is required toensure that the Link Manager 500 has properly conditioned MUDcandidates. The technique described below provides an accurate, realtime measure of MUD Processor 301, 302 performance.

The target E_(b)/N₀ for data UE connections in WCDMA is typically around3 dB. Therefore estimating the cancellation by measuring the datachannel before and after MUD processing is of limited utility, having adynamic range of only 3 dB. The pilot can be integrated across the slotyielding a signal to noise that is between 2 dB and 17 dB higher,depending upon spreading factor and DPCCH overhead. This provides auseful observable that is available on a slot by slot basis for somechannels.

An observable that is reliable for all channels can be computed from thedifference in the symbol estimates used at the MUD Processor 301, 302and those estimated post forward error correction. The estimated FECsymbols from the MUD Processor 301, 302 are sent to the Link Manager500, where they are compared to the FEC symbols from the convolutionaldecoder. There are three convolutional codes specified by the 3GPPdocument 3G TS 25.212. The code with the minimum gain is a ½ rateconstraint, length 9 convolutional code. At a E_(b)/N₀ of 3 dB, thiscode approximately 5.8 dB of coding gain, ensuring that the symbol errorrate from the convolutional decoder will be much lower than that fromthe MUD Processor 301, 302, and can be used as the “true” symbols todetect errors in the MUD Processor's 301, 302 symbol estimates.

This observable will have considerable latency however, ranging from 10to 80 msec, depending upon the depth of the FEC interleaving. Adetection of an error will result in an adjustment in the targetE_(b)/N₀ for the UE by the Link Manager 500.

Power Control

Also as part of preferred embodiments, the present invention includesmethods for tracking receiver performance and formulating power controlcommands based upon the performance estimates. While the introduction ofadvanced filtering techniques such as MUD and STAP offer the potentialfor increased network capacity and coverage, the realization of thatpotential is facilitated by innovative application of monitoring andcontrol functions to manage UE link parameters (e.g., commanded transmitpower, frame format) and receiver parameters (e.g., averaging windowlengths, filter tap support lengths). A preferred embodiment of thepresent invention, coordinated by the Link Manager 500, that seeks torealize this potential is described herein. Functional entities thatcomprise the Link Manager 500 are depicted in FIG. 18. Exploiting theuse of MUD and STAP incentivizes creative application of power controlmethods. Network capacity is dominated by the overall interferenceenvironment albeit with higher tolerance to interference with theapplication of advanced receiver techniques described herein.

Among the UE link parameter monitor and control functions coordinated bythe Link Manager 500 are three power control loops (in order oflatency): an outer power control loop, a second inner power control loopand a first inner power control loop. In preferred embodiments of thepresent invention, each loop is tightly integrated with the overallreceiver architecture.

The outer power control loop uses Data Quality Metrics (DQMs) derivedafter de-interleaving and decoding to set a base target E_(b)/N₀. Thelatency for the outer loop is driven by the de-interleaving processwhich requires up to eight frames (approximately 80 ms) of data.

The second inner power control loop estimates the gain afforded by theuse of STAP. This estimate is used to adjust the base target E_(b)/N₀for each UE connection. The latency of the second inner power controlloop is one frame (approximately 10 ms).

The first inner power control loop estimates each UE connection'sreceived E_(b)/N₀. The received E_(b)/N₀ is compared to the adjustedE_(b)/N₀ (or to the base E_(b)/N₀ when an adjusted E_(b)/N₀ is notavailable). If the received E_(b)/N₀ is less than the adjusted E_(b)/N₀(or base E_(b)/N₀, as appropriate), then the Transmit Power Control(TPC) bit is set to command an increase in the UE connection transmitpower.

The Outer Power Control Loop

In typical systems, a target E_(b)/N₀ is derived from the noisestatistics at the input to the decoder in conjunction with a desired biterror rate. For the same value of average SINR per bit, different noisestatistics will lead to differing variability in the instantaneous noiseper bit, and thus, bit error rate. Metrics available in the first innerpower control loop are not well suited for determining an appropriatebase target E_(b)/N₀; if for no other reason than that those metrics donot reflect the effect of coding. In addition, these metrics do notconsider a statistically large enough sample to reflect the noisestatistics. Fortunately, the noise statistics generally evolve moreslowly than the time scales of fast fading, whose consequences are afocus of the inner power control loops.

In preferred embodiments of the present invention, DQMs are determinedsubsequent to the decoding of data and are used to set a base targetE_(b)/N₀. One DQM to be determined is the Cyclic Redundancy Check (CRC).If an error is detected, the current base target E_(b)/N₀ is incrementeda substantial amount, e.g., 2 dB. Another DQM is the Cumulative ViterbiMetric (CVM) at the conclusion of the decoding block. Assuming Gaussianerror statistics, the CVM when suitably scaled represents an average SNRper bit over a block. That value is compared with the E_(b)/N₀ requiredto reach an acceptable bit error rate considering the convolutional codein use. The current base target E_(b)/N₀ is adjusted by any difference.Another DQM is the coherence time of the channel for each UE connection.Using the output of the STAP processor and the pilot bits in the DPCCH,the total impulse response of the system and channel for each UEconnection may be estimated at the symbol rate. The coherence of thechannel estimate over a set integration time, e.g., eight frames, iscomputed. This calculation is referred to as fading estimation and isperformed by the fading estimator module shown in FIG. 18. If the valuefalls below a set threshold, the channel is assumed to be a fadingchannel. The current base target E_(b)/N₀ is adjusted upward by anamount determined by the estimated coherence to account for differingE_(b)/N₀ requirements between Gaussian and fading channels.

The succession of DQM checks described here is illustrated in FIG. 21.

The First Inner Power Control Loop

This power control loop has the shortest latency. There are threecomponents of the first inner power control algorithm. First, usefulmeasures of received UE power are generated. Second, the UE SINR areestimated from these metrics. Finally, if UE connection SINR is lessthat the applicable target E_(b)/N₀, then a TPC (Transmit Power Control)bit is set in the next downlink dedicated physical control channel(DPCCH) slot. The measure of received power will be discussed first. SeeFIG. 19.

The measurement process relies on correlation of a known bit sequencewith the received data. Specifically, the 3-8 pilot bits (N_(pilot))embedded in the beginning of each uplink DPCCH slot may be used. Whilethe DPDCH and DPCCH are separately mapped to the In-phase (I) andQuadrature (Q) channels at the UE, the use of a complex scrambling codemixes them. Thus, before any portion of the uplink DPCCH can beprocessed, the received data is de-scrambled for each UE. In order forthe correlation peak to have a measurable gain over noise, the DPCCH isalso despread by the factor of 256.

Oversampling the received data yields robustness to fine synchronizationconcerns. As such, the sample rate of the correlation code is increasedto match that of the data using a band-limited interpolation filter. Thereceived data stream is then correlated with the known, spread,scrambled pilot chip sequence (the correlation code) over an interval ofdata centered on the known pilot sequence location and of length δ_(t).Correlation over a range of delays resolves multi-path delays at thechip rate and the real part gives the outputs of temporal Rake fingers.The correlation may be repeated for each available antenna, δ_(m), toyield additional Rake fingers. For each antenna, δ_(m), and delay,δ_(t), the N_(pilot) Rake fingers are then modulated by the known Pilotbits and summed. The δ_(t)*δ_(m), quantities are then processed toestimate the SINR for that user.

These steps are depicted in FIG. 19. Depending on the availableprocessing resources, the Rake algorithm may be a selection processwherein the finger with the largest power is used or a maximal-ratiocombining algorithm to coherently add the temporal and spatial diversitycomponents.

A preferred embodiment of the method illustrated in FIG. 19 may also bedescribed as generation of metrics for the inner loop power controlalgorithm. The signal from each antenna element is filtered, basebanded,sampled, and split into N_(users)*d_(t) parallel signals where N_(users)is the number of DPCCH channels to estimate and d_(t) is the number ofdelay taps per user. Using information from Layer 1, a code generatorprovides the appropriate OVSF scrambled with the complex conjugatescrambling code for each tap. The result of each path is integrated forone spreading code length duration. The resulting sum is the value atthe Rake finger which is sampled every symbol. One embodiment of theLink Manager would select the largest valued Rake finger for use in SINRestimation

While there are many known methods for converting the Rake fingeroutputs into a useful SINR estimate, a exemplary embodiment will bedescribed.

The Rake finger with the largest amplitude will be selected (i.e.antenna selection diversity). The ratio of that Rake finger power to theaverage power in the received data is related to the SINR per chip inthe selection Rake. If P_(uecon) is the UE DPCCH received power in thatfinger, the expected value of the ratio is,

$\begin{matrix}{R = {\frac{{\left( {N_{Pilot}S_{fcon}} \right)^{2}P_{uecon}} + {0.5N_{Pilot}{S_{fcon}\left( {I + N_{f}} \right)}}}{P_{uecon} + I + N_{f}}.}} & (10)\end{matrix}$

In Equation (10) S_(fcon) is the DPCCH spreading factor, I is thereceived interference power, and N_(f) is the thermal noise power in thechip sequence. The use of oversampling would affect the accuracy ofEquation (10) somewhat. The factor of 0.5 reflects the assumed evendistribution of energy between I and Q channels.

The quantity of interest is the signal to noise ratio per bit(E_(b)/N₀)_(D) in the DPDCH where N₀ represents the total non-signalpower in the received signal. (E_(b)/N₀)_(C) for the DPCCH can bewritten as,

$\begin{matrix}{\left( \frac{E_{b}}{N_{0}} \right)_{con} = {\frac{k_{con}S_{fcon}P_{uecon}}{\left( {I + N_{f}} \right)}.}} & (11)\end{matrix}$

In Equation (11), k_(con) is the inverse of the effective coding rate(accounting for puncturing) of the control channel.

The two preceding equations can be modified to relate the measuredquantity, R, to the desired quantity, (E_(b)/N₀)_(D) for the DPDCH.

$\begin{matrix}{\left( \frac{E_{b}}{N_{0}} \right)_{D} = {k_{data}{S_{fdata}\left( \frac{P_{uedata}}{P_{uecon}} \right)}{\frac{\left( {R - {0.5N_{Pilot}S_{fcon}}} \right.}{\left( {\left( {N_{Pilot}S_{f}} \right)^{2} - R} \right)}.}}} & (12)\end{matrix}$

The ratio of transmitted powers between the control and data channelsare used to yield a value appropriate for the DPDCH rather than for theDPCCH, which the measurement is based upon. The Network Layer sets thatratio. One could derive an uncertainty in the SNR/bit estimate from anuncertainty in R by modifying Equation (10) appropriately. IfR<<N_(pilot)S_(fcon), the uncertainty in SNR per bit is linearly relatedto the uncertainty in R.

Finally, Equation (12) is compared to a base target E_(b)/N₀ provided bythe outer power control loop (or an adjusted base target E_(b)/N₀ to bedescribed later in the disclosure). If the estimated E_(b)/N₀ is belowthe target, the TPC bit is set to command an increase in UE transmitpower in the next downlink slot. Otherwise, the TPC bit is set to zero.In the absence of MUD and STAP, Equation (13) is considered for each UE.

$\begin{matrix}\begin{matrix}{\delta \equiv {\left( \frac{E_{b}}{N_{0}} \right)_{target} - \left( \frac{E_{b}}{N_{0}} \right)_{D}}} \\{{\delta > 0},{{{TPC}\;{bit}} = 1}} \\{{\delta\underset{\_}{<}0},{{{TPC}\;{bit}} = 0}}\end{matrix} & (13)\end{matrix}$

Both MUD and STAP allow the network to operate with higher uplinkinterference levels than a conventional WCDMA network. To ensure thatthe quantity, R, in Equation (10) is accurately measured, it is usefulfor the power control algorithms to include processing to mitigate theincreased interference. As the anticipated processing load associatedwith MUD is substantially less than that of STAP, all MUD processingsteps (as described elsewhere in the disclosure) will be conducted apartfrom execution of the first inner power control loop. The MUD processorwill supply an estimate of the E_(b)/N₀ for cancelled UE connections andprovide that estimate to the Link Manager 500. The remaining UEconnections will have E_(b)/N₀ estimates per FIG. 18 and Equation (12).

For a four-element receive array, the STAP benefit is expected to beapproximately 4 dB of increased interference tolerance. The delaybetween receipt of the signal and completion of STAP processing,however, is expected to be approximately 10 ms. For UE connections withinsufficient diversity at the base station, such a delay wouldsubstantially limit the ability of the inner power control loop tocompensate for fast fading phenomena. In the absence of suchcompensation, these UE connections will require a higher target E_(b)/N₀than similar UE connections afforded some level of spatial and/ortemporal diversity thereby increasing the interference environment anddecreasing capacity. The Link Manager 500 provides three mechanisms forincreasing the accuracy of Equation (12) without the benefit of STAPgain. Depending on the level of STAP gain, one or more of these may beimplemented in a specific receiver design.

Restrict frame formats to those incorporating an increased number ofpilot bits (6-8) rather than the minimum (3).

Use the pilot bits of previous slots to increase the processing gain ofthe Rake fingers.

Define a small subset of STAP weights that may provide sufficient (butnot optimal) gain for all UE connections and apply these weights priorto the inner power control loop metric computation.

Restricting frame formats to those incorporating more than the minimumnumber of pilot bits will essentially remove the option of having UEconnections change data rates from frame to frame. That flexibilityrequires the use of Transport Format Combination Indicator bits whichtakes away from the number of bits allocated for the dedicated pilot.

Using pilot bits of previous slots to increase the processing gain ofRake fingers reduces the adaptation rate of the inner power control loopthereby somewhat degrading the ability to track fast fading.

Using a small subset of STAP weights requires an algorithm fordetermining a suitable subset of STAP weights along with the associatedcomputation penalty.

Different combinations of these methods may, in fact, be used fordifferent UE connections and the Link Manager 500 algorithm will directthe receiver to employ an appropriate set of methods for each UEconnection.

The aggregation of these processing steps comprises the first innerpower control loop and is schematically depicted in FIG. 20. In FIG. 20,the three methods of compensating for the lack of STAP gain in the innerloop power control algorithm are shown in terms of how the affect thepower control algorithm. Different users may use different combinationsof these methods. The Link Manager, using the fading rate estimator,will determine the optimal mix.

The Second Inner Power Control Loop

While the impact on SINR measurement accuracy of STAP is readily noted,a more subtle relationship between fast fading and STAP must also beaddressed. The effectiveness of STAP is attributable to its ability tocoherently combine spatial and temporal signals. As the propagationpaths of each UE connection fade, the effectiveness of STAP may varyaccordingly. If that variability is not tracked, the target E_(b)/N₀ ofeach UE connection must be set high enough to endure the minimum STAPeffectiveness. The argument is entirely parallel to why power control iseffect for reducing interference levels in the presence of fast fading.

To address this issue the SINR calculation performed on the Rake fingersfor the inner power control loop is duplicated on the output of the STAPprocessor for each UE connection.

The STAP processing gain (G_(STAP)), defined as the ratio of UE E_(b)/N₀at the output of the STAP processor to the UE E_(b)/N₀ in the selectedRake finger prior to STAP processing, is used to adjust the targetE_(b)/N₀ provided by the outer power control loop per Equation (14).

$\begin{matrix}\begin{matrix}{\delta \equiv {\left( \frac{E_{b}}{N_{0}} \right)_{target} - {\left( \frac{E_{b}}{N_{0}} \right)_{D}G_{STAP}}}} \\{{\delta > 0},{{{TPC}\;{bit}} = 1}} \\{{\delta\underset{\_}{<}0},{{{TPC}\;{bit}} = 0}}\end{matrix} & (14)\end{matrix}$

The goal of the first inner power control loop (post-MUD but pre-STAP)is to reach the target E_(b)/N₀ set after decoding less an estimatedvalue for STAP gain.

Initially, this estimated value will be equal to the number of channelelements. If the estimated SINR at the output of the STAP processor isnot equal to the outer loop target E_(b)/N₀, the estimated STAP gainused in the first loop is adjusted accordingly.

As an example, consider a pedestrian voice UE connection with an outerloop target E_(b)/N₀ of 6.7 dB. If the receive array consists of fourantennas, the initial STAP gain is assumed to be 6 dB and the modifiedtarget E_(b)/N₀ for the first inner power control loop is set at 0.7 dB.At the conclusion of STAP processing, if the estimated E_(b)/N₀ is 5.7dB, the STAP offset is reduced to 5 dB and the target E_(b)/N₀ for thefirst inner power control loop is increased to 1.7 dB.

The necessity of the first inner power control loop is dictated by thefading rate and the amount of spatial diversity available across thereceiver array. If the fading rate is slow enough, the additionallatency of basing power control decisions on post-STAP data isacceptable. Also, if the array aperture spans several spatial coherencelengths, the output of the STAP combiner will exhibit substantiallyreduced fast fading behavior. In either of these cases, the secondinner-loop power control algorithm will be able to compensate for anyremaining signal variability and the first inner power control loop maybe de-activated for a particular UE. That determination is made by theLink Manager 500 using the UE channel fading estimation algorithm.

As examples, the fading time scales of a slowly walking pedestrian caneasily exceed 10 ms (one frame). In that case, the low latency of thefirst inner power control loop calculation is not required and the TPCbit for that UE connection may be set based on the second inner powercontrol loop calculation with a latency typically less than 10 ms. Thisreduces computational complexity thereby freeing processing resourcesfor other activities.

Alternatively, the fading time scales of a moderate speed vehicle (e.g.60 km/h) can be on the order of several ms. In that case, the lowlatency of the first inner power control loop calculation is necessaryif there is inadequate spatial diversity across the receiver array tomitigate the fast fading effects. The ability of embodiments of thisinvention to adapt the power control strategy for each UE connectionwhile remaining integrated with the advanced filtering strategies isnovel accomplishment in the field of wireless spread spectrumcommunications.

Power Control for MUD Processor-Controlled Channels

The power control structure described thus far is applied to UE channelsnot handled by a MUD Processor 301, 302. Power control for the MUDchannels is also important. Thus, each MUD Processor 301, 302incorporates the functionality of the second inner power controlalgorithm directly into the channel estimation process. The processor301, 302 generates and passes E_(b)/N₀ estimates directly to the LinkManager 500 on a slot-to-slot basis where the estimates are compared totargets in the manner described earlier. As the latency associated withthe MUD Processor 301, 302 is not anticipated to exceed a one to twoslot duration, the first inner power control algorithm is not requiredand an explicit STAP gain estimate is not needed.

UE symbol estimates for UE connections are passed to a module thatimplements de-interleaving and decoding. The outer power controlalgorithm thus makes no distinction between MUD channels and non-MUDchannels.

Power Control for High and Medium Data Rate Connections

Preferred embodiments of the invention include a method for improvingMUD Processor 301, 302 performance by providing conditioned powercontrol inputs on the high and medium data rate connections.

The MUD Processor 301, 302 cancels UE channels designated by the MUDController 400. Network performance is degraded if the transmit power ofthese connections is either too low or too high. Latency concernsdiscourage the use of error correction coding algorithms in the MUDprocess. Nevertheless, the impact of incorrect symbol decisions issignificant and may, in fact, increase rather than decrease theinterference environment for other UE channels. If the transmit power ofthese UE connections is inadequate, incorrect symbol decisions will bemade and network performance will suffer. Note that cancellationperformance increases nearly linearly with UE transmit power. Thus, anexcessive transmit power will not have a deleterious effect oncancellation efficiency; however the excess will lead to unnecessarilyhigh interference levels in adjacent cells that are not using MUDprocessing on the particular UE channel. To improve network performance,UE channels designated for cancellation by the MUD Processor 301, 302are directed to specific higher transmit powers than would otherwise bethe case. The Link Manager 500 has access to a list of UE allocated toeach MUD Processor 301, 302. The target E_(b)/N₀ for these UEconnections is initially increased by a pre-determined amount; Table 9is an example of target E_(b)/N₀ that have been adjusted for the MUDpower premium for UE connections designated for MUD cancellation. Thisincrease is in addition to all power control processes describedearlier.

The MUD power premium is intended, among other purposes, to promotereliable symbol decoding. If symbol errors are being made the powerpremium must be incremented. As a continual check on the reliabilitylevel, the symbol estimates and the convolutional decoder symbolestimates for the MUD channels are compared at the conclusion of eachinterleaving period. For this purpose, MUD channel symbol estimates aredirected to the Link Manager 500 where they are compared to post-errorcorrection coding symbol estimates received from the convolutionaldecoder. If any discrepancies are found, the MUD power premium for thatUE connection is increased by an amount related to the number of errorsdiscovered. As was noted earlier, the MUD Processor 301, 302 performscancellation efficiency checks on a slot-to-slot basis and may alsorequest an alteration in the MUD power premium.

The Link Manager 500 is also responsible for providing a UE database tothe MUD Processor 301, 302. Specifically, this database includes, but isnot limited to, the estimated received power, the data rate, andinformation required to generate scrambling and spreading codes for eachUE active in the sector.

The preceding paragraphs describe a variety of inputs that are used bythe Link Manager 500 in determining whether to set each UE TransmissionPower Control (TPC) bit to one or zero. The general approach fordetermining the appropriate value for the TPC bit of each UE is capturedin Equation (15).

$\begin{matrix}\begin{matrix}{\delta \equiv {\left( \frac{E_{b}}{N_{0}} \right)_{target} + \Delta_{\underset{Premium}{MUD}} - {\left( \frac{E_{b}}{N_{0}} \right)_{D}G_{STAP}}}} \\{{\delta > 0},{{{TPC}\;{bit}} = 1}} \\{{\delta\underset{\_}{<}0},{{{TPC}\;{bit}} = 0}}\end{matrix} & (15)\end{matrix}$

Multiplexing UE Connections

Several embodiments of the invention include a technique for timemultiplexing a single high data rate connection over several connectionsthat require lower data rates, making the collection of connections looklike a single high data rate connection to the MUD Processor 301, 302.

While one role of the Link Manager 500 is to control UE transmit powerto optimize the combined MUD/STAP performance of the receiver, preferredembodiments of the invention also exert control over UE frame format intwo manners to improve receiver performance.

The first technique arises from the observation that while much of theprocessing required for a given MUD channel is independent of thechannel data rate, the MAI contribution is almost entirely dependent onthe data rate. This asymmetry may be exploited by directing several lowdata rate UE connections to sequentially burst at higher data rates in acontrolled, time-multiplexed manner thereby allowing the MUD Processor301, 302 to suppress more interference under a given computationalconstraint.

This technique enables the efficient allocation of MUD processingresources for various distributions of UE quality of service mixes. Incases where the medium or low data rate UE connections dominate the MAIenvironment, for example where there are no high data rate UEconnections, or the population of the low rate UE connections is largeenough to overcome the differences in spreading gain, this techniquewill improve the overall receiver performance. There are fourcharacteristics of selected UE that allow this approach to be effective.The UE to be multiplexed must have coherent, non-fading channels. Thechannel coherence is estimated in the outer power control loop acrosseight frames. The UE to be multiplexed must have stable STAP weightsacross the multiplexing time if a fully block-adaptive approach for STAPis not implemented. The UE to be multiplexed must have QoS requirementsthat are tolerant of data bursts, and hardware with the ability tobuffer data. The latency associated with multiplexing grows linearlywith the number of UE sharing the channel because the data rates of eachUE can only be changed on a frame by frame basis. The UE to bemultiplexed must have implemented the WCDMA standards for the requiredburst rate, and for frame by frame data rate adjustment.

Once the quantity of available MUD processing resources has beendetermined by the MUD Controller 400 and communicated to the LinkManager 500, and a subset of the UE meeting these requirements has beendetermined, the UE are sorted by their current MAI contribution. The MAIdata are also supplied by the MUD Controller 400.

The Link Manager 500 begins the time multiplexing by having the UE withthe greatest MAI contribution burst data on the first frame using asingle DPDCH channel and a spreading factor of four (4). If the LinkManager 500 verifies effective cancellation for this single DPDCHchannel, two options are then considered. First, if the UE currentlybeing cancelled has additional active DPDCH channels, these areredirected to the single time multiplexed channel being cancelled.Second, other active UE data channels on the MUD candidate list areredirected to a time-multiplexed channel. The first option is preferredas the incremental processing required is less than that for the secondoption.

The list of candidate UE for time multiplexing is updated on a frame byframe basis. UE are removed from the candidate list for ending a call,channel fading, unstable STAP weights (i.e., excessive motion), orchanging latency requirements.

Data Rate Ramp Up

Further embodiments of the invention include a technique that forces ahigh data rate connection request to initiate with a lower data rate andramp up to the requested rates as the STAP and MUD filters settle.

Depending on the estimated fading rate of a UE channel, the STAP and MUDfilters may be estimated based on several slots of data. This departurefrom strictly block-based filtering introduces the potential fortransient periods of poor filtering performance immediately proceedingcall initiation. The consequences of the start-up transient are greaterfor high data rate UE connections than low data rate UE connectionsowing to their increased transmit power. In order to mitigate thedeleterious effect on mutual access interference, the Link Manager 500denies high data rate service to a UE until it ascertains a sufficientperiod has passed to ensure filter stability, e.g., one frame or 10 ms.

In one embodiment of this “Quiet Start” method, the Link Manager 500buffers the first frame of transport channel data for a high data rateUE connection. The mapping of the transport channel to the physicalchannel for the first channel is restricted to orthogonal variablespreading factor (OVSF) codes of length 128 or greater for the firstuplink frame. Subsequent frames use an OVSF code appropriate to therequested data rate. As such, the Link Manager 500 buffers approximatelyone frame worth of data for that UE connection in a first-in first-out(FIFO) buffer which is emptied when the demanded data rate decreasessufficiently as with a Discontinuous Transmission (DTX) or calltermination.

Out-of-Cell Connections

A technique for requesting increased radiated power on out-of-cell dataconnections to support MUD processing on these connections is alsoincluded in preferred embodiments of the invention.

As discussed earlier, UE channels to be demodulated using the MUDProcessor 301, 302 must be directed to higher transmit powers (the MUDpower premium) to support reliable symbol decoding without recourse tothe convolutional decoder. If the prospective UE MUD candidate iscontrolled by another base station, i.e., an out-of-cell UE connectionas is the case in soft-handoff, the power control command issued by thelocal base station may not induce the UE to increase transmit power.There are two scenarios to be examined. First, both base stations mayemploy the receiver disclosed in this patent. Second, the other basestation may not employ the receiver.

In the first case, the Link Manager 500 algorithms in both base stationsexchange lists of UE channels that are candidates for MUD processing. Ifa candidate UE appears in both lists, the Link Manager 500 accepts theUE for MUD Processor 301, 302 and each Link Manager 500 commands a powerpremium. The UE interference is then cancelled in both receivers using aMUD Processor 301, 302. If a candidate UE is absent from either list,the UE is determined to be ineligible for MUD processing and removedfrom the list.

In the second case, where the other receiver employs a foreignarchitecture, out-of-cell UE connections are considered ineligible forlocal MUD processing. The underlying criterion is that a UE will notincrease power unless its local cell is capable of canceling theincreased interference.

Estimating Channel Fading Rate

In another aspect, the invention includes systems and methods forestimating channel fading rate which are used in turn to set rateparameters and averaging intervals for the adaptive STAP, MUD, and powercontrol algorithms.

Adaptive signal processing algorithm performance is closely tied to thecomplexity and fading characteristics of the signal to be estimated.Knowledge of key channel parameters such as coherence time and totaldelay spread facilitate optimal selection of adaptive algorithmparameters such as tap delay support, adaptation rate factors, andweight update algorithm.

In addition to the E_(b)/N₀ estimates from the power control loops, inpreferred embodiments of the present invention, the Link Manager 500receives the Rake finger outputs for each UE channel for each slot.These quantities are buffered for a predetermined amount of time, T(e.g. 80 ms). Two primary quantities are computed.

The channel coherence as a function of delay is computed as in Equation(16) where F is the complex valued Rake finger with the largestamplitude in the current slot. While the quantity is defined usingcontinuous-time notation, the implementation could readily be indiscrete-time form. The channel delay spread structure, S_(m)[n] isestimated by identifying all Rake finger outputs for antennas m anddelays n in the current slot with amplitudes greater than, for example,¼ of the peak Rake finger amplitude. This is a binary vector.

$\begin{matrix}{{C(\tau)} = {\frac{\int_{- T}^{0}{{F(t)}{F^{*}\left( {t - \tau} \right)}\ {\mathbb{d}t}}}{\int_{- T}^{0}{{F(t)}{F^{*}(t)}\ {\mathbb{d}t}}}}} & (16)\end{matrix}$

The channel fading rate is defined as the inverse of the delay,τ_(fade), at which the channel coherence falls below a definedthreshold, e.g. 0.5. This value is used to support four Link Manager 500decisions. First, for block adaptive filtering strategies, the blocklength for a UE channel is restricted to less than τ_(fade). Forrecursive filtering strategies, the adaptation rate for a UE channel isrestricted to greater than 1/τ_(fade). FIG. 22 a graphically illustratesthis process. This allows the maximum coherent processing gain forweight calculation. Second, the fading rate estimate is used todetermine UE eligibility for the data UE connection time multiplexingoption described earlier. Third, the fading rate is used in setting thetarget E_(b)/N₀ for a UE connection. UE channels with substantial fadingon a slot-to-slot basis are assigned an additional margin to theirE_(b)/N₀ target value. Conversely, stable UE channels require lesstransmit power. Finally, if the value of τ_(fade) is greater than adetermined threshold for a given UE channel, that UE may be excludedfrom the first inner power control loop calculation thereby savingprocessing cost.

The channel delay spread is defined as the maximum duration betweennon-zero entries in the channel delay spread structure vector, S_(m)[n].For non-sparse filters, the maximum tap delay is set to this value. Forsparse filters, taps are placed at delays with non-zero entriesS_(m)[n]. FIG. 22 b graphically illustrates this process.

In preferred embodiments, the processes illustrated in FIG. 22 show thechannel self-coherence function and delay spread spectrum function. Thechannel self-coherence function, C, is computed as a function of delay,τ, for each UE physical channel. The coherence time, τ_(coh), is definedto be the delay at which C falls below a pre-determined threshold, e.g.0.5. The fading rate is the inverse of the coherence time. The channeldelay spread structure function, S_(m)[n], for each antenna, m, and chipdelay, n, is computed by thresholding the impulse response estimate. Inthis case, all tap delays with an amplitude within 0.5 of the maximumare tagged. For those n, S_(m)[n]=1. For all others, S_(m)[n]=0.

Description of a System of the Invention Implementation

FIG. 23 depicts the high-level design for a Reverse Link ProcessingSystem 900 of the present invention. Figures of the type of FIG. 23 areknown as structure diagrams. The figure includes connected componentswith well-defined interfaces (or “ports”) and associated protocols,e.g., signal sets. Preferred embodiments of the invention disclosedabove are contained within the Reverse Link Processor 910 and SlotProcessor 920 components. In general, data flows through these structurediagrams from left to right, control flows down, and effectivenessmeasures flow up; this convention is maintained throughout this section(although it is not a standard for all structure diagrams).

The RF Processor 930 performs analog-to-digital (A/D) conversion,down-conversion to baseband, oversampling, the combination of thesignals from each antenna element into a single array structure, andseparation of the resulting data stream into slots for subsequentprocessing by the Slot Processors 920.

The Post Processor 940 receives user symbol data (for each channel) on aslot-by-slot basis, combines slots to form symbol streams, and performsdecoding and de-interleaving on these symbol streams. The remainder ofthis discussion is concentrated on the Reverse Link Processor 910 andSlot Processor 920 components.

The structure of the Reverse Link Processor 910 is shown in FIG. 24. TheReverse Link Controller 911 maintains the table of user equipment (UE)properties (including UE identity, scrambling code information, channelconfiguration, transmit power, target E_(b)/N₀, and estimated E_(b)/N₀)and provides that information to lower control layers for signalprocessor slot- and stage-specific configuration. It also determines thevalue of the power control command (transmit power control bit, or TCP)to be sent to each UE via the forward link. The Slot ProcessorController 912 manages slot availability, directs the Slot ProcessorSwitch 913 to allocate incoming data slots to specific (idle) SlotProcessors 920, delivers slot setup information to Slot Processors 920,and receives processing results from the Slot Processor 920 s. Each SlotProcessor 920, as illustrated in FIG. 25, processes a slot of data at atime for all users. In this way, each Slot Processor 920 worksconcurrently on a different slot of data. The symbol data output by theSlot Processors 920 is combined by the Symbol Data Concentrator 914 andrelayed to the Post Processor 940.

Each Slot Processor 920 comprises three Stage Processors 921. The SlotProcessor 920 provides specific configuration information to each StageProcessor 921, including the list of UE to process (and possibly cancel)in each stage. The Slot Processor 921 also consolidates effectivenessmeasures from each stage and delivers them to the Slot ProcessorController 912 above. The Acquisition Processor 922 looks for new usersin the current slot. Each Stage Processor 921 assumes a different rolebased on its context (as defined by its stage) and connections. TheStage Processor 921 internal structure is depicted in FIG. 26. Within astage, a separate User Processor 923 processes the slot data to extractthe symbols for each UE. In the first two stages of processing onlythose users that are to be cancelled with MUD are processed; the MUDcancellation is performed within these stages as well. All remaining(un-cancelled) UE are processed in the third stage. Within the StageProcessor 921, the Covariance Computer 924 is used to generate or updatea global covariance matrix for use in STAP processing as well as invertit. The Estimate Subtractor 926 is an optional component that subtractsindividual UE data estimates (computed by the MUD Processor 301, 302)from the slot data, in the first two processing stages only, to supportMUD cancellation. The remaining Stage Processor 921 components are usedto replicate or merge data, symbols, or channel estimates; the purposes,characteristics, and implementation of these components is known tothose skilled in the art.

Each User Processor 923 performs up to three phases of processing on itscorresponding UE data: STAP, demodulation, and, optionally, MUDcancellation (in stages 1 and 2). The relationship between these phasesis depicted in FIG. 27. The MUD Processor 301, 302 component is optionaland is only created for User Processor 923 s within Stage Processors 920that are plugged into stage 1 or stage 2 roles. The Long Code Generator927 component generates the segment of the long code appropriate for theUE and time of interest.

It should be noted that the design described herein does not have anobvious one-to-one correspondence with all of the functions described inthe claims. In particular, the functionality of the MUD Controller 301,302 and Link Manager 500 are not allocated to separable components inthe design. The functions of these logical components are distributedacross several design layers as part of the Reverse Link Controller 911and the Slot Processor Controller 912.

1. In a communications network comprising a base station and a pluralityof user equipment (UE), the network having at least one radio frequencycommunications path between each UE and the base station, the basestation comprising a plurality of antennas and at least one base stationreceiver, a method of reducing multiple access interference in at leastone radio frequency communications path between the base station and atleast one UE, the method comprising: interleaving space-time adaptiveprocessing (STAP) and multi-user detection (MUD) in a first plurality ofstages in a base station receiver, by: selecting at least one UEconnection for MUD processing by stage, and in a second plurality ofstages: demodulating the UE connections selected for MUD processing in acurrent stage, the demodulation employing STAP, and performing MUDcancellation on UE connections selected for MUD processing in thecurrent stage; and acquiring new UE connections after at least one stageof MUD cancellation; and commanding transmit power for each UEconnection; at least one UE connection being characterized by a targetE_(b)/N₀ at least equal to a corresponding threshold target E_(b)/N₀,and selecting UE connections for MUD processing by stage comprisesestimating the multiple access interference contribution of each UEconnection having a target E_(b)/N₀ at least equal to the correspondingthreshold target E_(b)/N₀; and UE connections characterized by a targetE_(b)/N₀ at least equal to a corresponding threshold target E_(b)/N₀ areselected for MUD processing in order of descending multiple accessinterference contribution.
 2. The method recited in claim 1, the atleast one stage of MUD cancellation is a final stage within the secondplurality of stages.
 3. The method recited in claim 1, performing MUDcancellation further comprises group-wise subtractive interferencecancellation.
 4. The method recited in claim 3, the group-wisesubtractive interference cancellation is group-wise serial subtractiveinterference cancellation.
 5. The method recited in claim 1, performingMUD cancellation further comprises canceling each UE connection selectedfor canceling down to the ambient multiple access interference level ofvoice UE connections in the network.
 6. The method recited in claim 1wherein the threshold target E_(b)/N₀ for each UE connection is afunction of a spreading factor of the UE connection.
 7. The methodrecited in claim 1 further comprising: determining data rates for UEconnections; and if no high data rate UE connections then commanding oneor more low rate burst-enabled UE connections to sequentially burst athigher data rates.
 8. The method recited in claim 2 further comprising:after the final stage, demodulating remaining UE connections.
 9. Themethod recited in claim 1 wherein commanding transmit power for each UEconnection further comprises: determining a base target E_(b)/N₀ as afunction of at least one data quality metric (DQM); estimating areceived E_(b)/N₀ for each UE connection, and for each UE connectiondesignated for MUD processing, adjusting the base target E_(b)/N₀upward; for each UE connection not designated for MUD processing, wherethe estimated E_(b)/N₀ is less than the base target E_(b)/N₀, commandingincreased UE connection transmit power; and for each UE connectiondesignated for MUD processing, where the estimated E_(b)/N₀ is less thanthe adjusted base target E_(b)/N₀, commanding increased UE connectiontransmit power.
 10. In a communications network comprising a basestation and a plurality of user equipment (UE), the network having atleast one radio frequency communications path between each UE and thebase station, the base station comprising a plurality of antennas and atleast one base station receiver, a method of reducing multiple accessinterference in at least one radio frequency communications path betweenthe base station and at least one UE, the method comprising:interleaving space-time adaptive processing (STAP) and multi-userdetection (MUD) in a first plurality of stages in a base stationreceiver, by: selecting at least one UE connection for MUD processing bystage by: multiplexing a plurality of UE connections into an aggregateUE connection, and performing MUD cancellation on the aggregate UEconnection as on a non-aggregate user system connection; and in a secondplurality of stages: demodulating the at least one UE connectionselected for MUD processing in a current stage, the demodulationemploying STAP, and performing MUD cancellation on UE connectionsselected for MUD processing in the current stage; acquiring new UEconnections after at least one stage of MUD cancellation; and commandingtransmit power for each UE connection; performing MUD cancellation on UEconnections further comprises: at least one UE connection beingcharacterized by a target E_(b)/N₀ less than a corresponding thresholdtarget E_(b)/N₀, and selecting at least one UE connection for MUDprocessing by stage comprises: increasing the target E_(b)/N₀ of atleast one UE connection having the lowest spreading factor from amongthe UE connections characterized by a target E_(b)/N₀ less than acorresponding threshold target E_(b)/N₀, by an amount sufficient tocharacterize the at least one UE connection as having an increasedtarget E_(b)/N₀ at least equal to a corresponding threshold targetE_(b)/N₀.
 11. The method recited in claim 10, the at least one stage ofMUD cancellation is a final stage within the second plurality of stages.12. The method recited in claim 11 further comprising: after the finalstage, demodulating remaining UE connections.
 13. The method recited inclaim 10: the set of UE connections selected for MUD processing arecharacterized by a range of target E_(b)/N₀ not greater than 3 dB. 14.The method recited in claim 10, commanding transmit power for each UEconnection further comprises: determining a base target E_(b)/N₀ as afunction of at least one data quality metric (DQM); estimating areceived E_(b)/N₀ for each UE connection, estimating a STAP gain foreach UE connection, and adjusting the base target E_(b)/N₀ down by theamount of estimated STAP gain for each UE connection where the estimatedE_(b)/N₀ is less that the adjusted base target E_(b)/N₀, commandingincreased UE connection transmit power.
 15. The method recited in claim10 wherein performing MUD cancellation further comprises: group-wisesubtractive interference cancellation.
 16. The method recited in claim15 wherein the group-wise subtractive interference cancellation isgroup-wise serial subtractive interference cancellation.
 17. The methodrecited in claim 10 further comprising: determining data rates for UEconnections; and if no high data rate UE connections then command one ormore low rate burst-enabled UE connections to sequentially burst athigher data rates.
 18. In a communications network comprising a basestation and a plurality of user equipment (UE), the network having atleast one radio frequency communications path between each UE and thebase station, the base station comprising a plurality of antennas and atleast one base station receiver, a method of reducing multiple accessinterference in at least one radio frequency communications path betweenthe base station and at least one UE, the method comprising:interleaving space-time adaptive processing (STAP) and multi-userdetection (MUD) in a first plurality of stages in a base stationreceiver, by: selecting at least one UE connection for MUD processing bystage, and in a second plurality of stages: demodulating the UEconnections selected for MUD processing in a current stage, thedemodulation employing STAP, and performing MUD cancellation on UEconnections selected for MUD processing in the current stage; andacquiring new UE connections after at least one stage of MUDcancellation; and commanding transmit power for each UE connection;wherein commanding transmit power for each UE connection furthercomprises: determining a base target E_(b)/N₀ as a function of at leastone data quality metric (DQM); estimating a received E_(b)/N₀ for eachUE connection, and for each UE connection designated for MUD processing,adjusting the base target E_(b)/N₀ upward; for each UE connection notdesignated for MUD processing, where the estimated E_(b)/N₀ is less thanthe base target E_(b)/N₀, commanding increased UE connection transmitpower; and for each UE connection designated for MUD processing, wherethe estimated E_(b)/N₀ is less than the adjusted base target E_(b)/N₀,commanding increased UE connection transmit power.
 19. In acommunications network comprising a base station and a plurality of userequipment (UE), the network having at least one radio frequencycommunications path between each UE and the base station, the basestation comprising a plurality of antennas and at least one base stationreceiver, a method of reducing multiple access interference in at leastone radio frequency communications path between the base station and atleast one UE, the method comprising: interleaving space-time adaptiveprocessing (STAP) and multi-user detection (MUD) in a first plurality ofstages in a base station receiver, by: selecting at least one UEconnection for MUD processing by stage by: multiplexing a plurality ofUE connections into an aggregate UE connection, and performing MUDcancellation on the aggregate UE connection as on a non-aggregate usersystem connection; and in a second plurality of stages: demodulating theat least one UE connection selected for MUD processing in a currentstage, the demodulation employing STAP, and performing MUD cancellationon UE connections selected for MUD processing in the current stage;acquiring new UE connections after at least one stage of MUDcancellation; and commanding transmit power for each UE connection;commanding transmit power for each UE connection further comprises:determining a base target E_(b)/N₀ as a function of at least one dataquality metric (DQM); estimating a received E_(b)/N₀ for each UEconnection, estimating a STAP gain for each UE connection, and adjustingthe base target E_(b)/N₀ down by the amount of estimated STAP gain foreach UE connection where the estimated E_(b)/N₀ is less that theadjusted base target E_(b)/N₀, commanding increased UE connectiontransmit power.